add: async inference stack

README.md

@@ -360,7 +360,7 @@ with profile(
 If you want, you can cite this work with:
 ```bibtex
 @misc{cadene2024lerobot,
-    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Wolf, Thomas},
+    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascale, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
     title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
     howpublished = "\url{https://github.com/huggingface/lerobot}",
     year = {2024}

docs/source/installation.mdx

@@ -55,7 +55,7 @@ conda install ffmpeg -c conda-forge
 
 Install 🤗 LeRobot:
 ```bash
-cd lerobot && pip install ".[feetech]"
+cd lerobot && pip install -e ".[feetech]"
 ```
 
 ## Troubleshooting

examples/11_use_moss.md

@@ -141,7 +141,7 @@ python lerobot/scripts/configure_motor.py \
   --ID 1
 ```
 
-Note: These motors are currently limitated. They can take values between 0 and 4096 only, which corresponds to a full turn. They can't turn more than that. 2048 is at the middle of this range, so we can take -2048 steps (180 degrees anticlockwise) and reach the maximum range, or take +2048 steps (180 degrees clockwise) and reach the maximum range. The configuration step also sets the homing offset to 0, so that if you misassembled the arm, you can always update the homing offset to account for a shift up to ± 2048 steps (± 180 degrees).
+Note: These motors are currently limited. They can take values between 0 and 4096 only, which corresponds to a full turn. They can't turn more than that. 2048 is at the middle of this range, so we can take -2048 steps (180 degrees anticlockwise) and reach the maximum range, or take +2048 steps (180 degrees clockwise) and reach the maximum range. The configuration step also sets the homing offset to 0, so that if you misassembled the arm, you can always update the homing offset to account for a shift up to ± 2048 steps (± 180 degrees).
 
 Then unplug your motor and plug the second motor and set its ID to 2.
 ```bash

examples/12_use_so101.md

@@ -61,7 +61,7 @@ conda install ffmpeg -c conda-forge
 
 Install 🤗 LeRobot:
 ```bash
-cd lerobot && pip install ".[feetech]"
+cd lerobot && pip install -e ".[feetech]"
 ```
 
 > [!NOTE]

lerobot/common/datasets/image_writer.py

@@ -106,7 +106,7 @@ def worker_process(queue: queue.Queue, num_threads: int):
 class AsyncImageWriter:
     """
     This class abstract away the initialisation of processes or/and threads to
-    save images on disk asynchrounously, which is critical to control a robot and record data
+    save images on disk asynchronously, which is critical to control a robot and record data
     at a high frame rate.
 
     When `num_processes=0`, it creates a threads pool of size `num_threads`.

lerobot/common/datasets/lerobot_dataset.py

@@ -944,7 +944,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
     def stop_image_writer(self) -> None:
         """
         Whenever wrapping this dataset inside a parallelized DataLoader, this needs to be called first to
-        remove the image_writer in order for the LeRobotDataset object to be pickleable and parallelized.
+        remove the image_writer in order for the LeRobotDataset object to be picklable and parallelized.
         """
         if self.image_writer is not None:
             self.image_writer.stop()

lerobot/common/datasets/video_utils.py

@@ -101,7 +101,7 @@ def decode_video_frames_torchvision(
     keyframes_only = False
     torchvision.set_video_backend(backend)
     if backend == "pyav":
-        keyframes_only = True  # pyav doesnt support accuracte seek
+        keyframes_only = True  # pyav doesn't support accurate seek
 
     # set a video stream reader
     # TODO(rcadene): also load audio stream at the same time

lerobot/common/policies/pi0/modeling_pi0.py

@@ -357,7 +357,7 @@ class PI0Policy(PreTrainedPolicy):
             if self.config.resize_imgs_with_padding is not None:
                 img = resize_with_pad(img, *self.config.resize_imgs_with_padding, pad_value=0)
 
-            # Normalize from range [0,1] to [-1,1] as expacted by siglip
+            # Normalize from range [0,1] to [-1,1] as expected by siglip
             img = img * 2.0 - 1.0
 
             bsize = img.shape[0]

lerobot/common/policies/pi0fast/modeling_pi0fast.py

@@ -516,7 +516,7 @@ class PI0FAST(nn.Module):
                         interpolate_like_pi=self.config.interpolate_like_pi,
                     )
 
-                # Normalize from range [0,1] to [-1,1] as expacted by siglip
+                # Normalize from range [0,1] to [-1,1] as expected by siglip
                 img = img * 2.0 - 1.0
 
                 bsize = img.shape[0]

lerobot/common/robot_devices/control_utils.py

@@ -243,6 +243,11 @@ def control_loop(
 
     timestamp = 0
     start_episode_t = time.perf_counter()
+
+    # Controls starts, if policy is given it needs cleaning up
+    if policy is not None:
+        policy.reset()
+
     while timestamp < control_time_s:
         start_loop_t = time.perf_counter()
 

lerobot/scripts/server/async_inference.proto

@@ -0,0 +1,60 @@
+// fmt: off
+// flake8: noqa
+// !/usr/bin/env python
+
+//  Copyright 2024 The HuggingFace Inc. team.
+//  All rights reserved.
+
+//  Licensed under the Apache License, Version 2.0 (the "License");
+//  you may not use this file except in compliance with the License.
+//  You may obtain a copy of the License at
+
+//      http://www.apache.org/licenses/LICENSE-2.0
+
+//  Unless required by applicable law or agreed to in writing, software
+//  distributed under the License is distributed on an "AS IS" BASIS,
+//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//  See the License for the specific language governing permissions and
+//  limitations under the License.
+syntax = "proto3";
+
+package async_inference;
+
+// AsyncInference: from Robot perspective
+// Robot send observations to & executes action received from a remote Policy server
+service AsyncInference {
+  // Robot -> Policy to share observations with a remote inference server
+  // Policy -> Robot to share actions predicted for given observations
+  rpc SendObservations(stream Observation) returns (Empty);
+  rpc StreamActions(Empty) returns (stream Action);
+  rpc SendPolicyInstructions(PolicySetup) returns (Empty);
+  rpc Ready(Empty) returns (Empty);
+}
+
+enum TransferState {
+    TRANSFER_UNKNOWN = 0;
+    TRANSFER_BEGIN = 1;
+    TRANSFER_MIDDLE = 2;
+    TRANSFER_END = 3;
+}
+
+// Messages
+message Observation {
+  // sent by Robot, to remote Policy
+  TransferState transfer_state = 1;
+  bytes data = 2;
+}
+
+message Action {
+  // sent by remote Policy, to Robot
+  TransferState transfer_state = 1;
+  bytes data = 2;
+}
+
+message PolicySetup {
+  // sent by Robot to remote server, to init Policy
+  TransferState transfer_state = 1;
+  bytes data = 2;
+}
+
+message Empty {}

lerobot/scripts/server/async_inference_pb2.py

@@ -0,0 +1,48 @@
+# fmt: off
+# flake8: noqa
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# NO CHECKED-IN PROTOBUF GENCODE
+# source: async_inference.proto
+# Protobuf Python Version: 5.29.0
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import runtime_version as _runtime_version
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+_runtime_version.ValidateProtobufRuntimeVersion(
+    _runtime_version.Domain.PUBLIC,
+    5,
+    29,
+    0,
+    '',
+    'async_inference.proto'
+)
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x61sync_inference.proto\x12\x0f\x61sync_inference\"S\n\x0bObservation\x12\x36\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x1e.async_inference.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"N\n\x06\x41\x63tion\x12\x36\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x1e.async_inference.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"S\n\x0bPolicySetup\x12\x36\n\x0etransfer_state\x18\x01 \x01(\x0e\x32\x1e.async_inference.TransferState\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\"\x07\n\x05\x45mpty*`\n\rTransferState\x12\x14\n\x10TRANSFER_UNKNOWN\x10\x00\x12\x12\n\x0eTRANSFER_BEGIN\x10\x01\x12\x13\n\x0fTRANSFER_MIDDLE\x10\x02\x12\x10\n\x0cTRANSFER_END\x10\x03\x32\xa9\x02\n\x0e\x41syncInference\x12J\n\x10SendObservations\x12\x1c.async_inference.Observation\x1a\x16.async_inference.Empty(\x01\x12\x42\n\rStreamActions\x12\x16.async_inference.Empty\x1a\x17.async_inference.Action0\x01\x12N\n\x16SendPolicyInstructions\x12\x1c.async_inference.PolicySetup\x1a\x16.async_inference.Empty\x12\x37\n\x05Ready\x12\x16.async_inference.Empty\x1a\x16.async_inference.Emptyb\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'async_inference_pb2', _globals)
+if not _descriptor._USE_C_DESCRIPTORS:
+  DESCRIPTOR._loaded_options = None
+  _globals['_TRANSFERSTATE']._serialized_start=301
+  _globals['_TRANSFERSTATE']._serialized_end=397
+  _globals['_OBSERVATION']._serialized_start=42
+  _globals['_OBSERVATION']._serialized_end=125
+  _globals['_ACTION']._serialized_start=127
+  _globals['_ACTION']._serialized_end=205
+  _globals['_POLICYSETUP']._serialized_start=207
+  _globals['_POLICYSETUP']._serialized_end=290
+  _globals['_EMPTY']._serialized_start=292
+  _globals['_EMPTY']._serialized_end=299
+  _globals['_ASYNCINFERENCE']._serialized_start=400
+  _globals['_ASYNCINFERENCE']._serialized_end=697
+# @@protoc_insertion_point(module_scope)

lerobot/scripts/server/async_inference_pb2_grpc.py

@@ -0,0 +1,236 @@
+# fmt: off
+# flake8: noqa
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+import warnings
+
+import async_inference_pb2 as async__inference__pb2
+
+GRPC_GENERATED_VERSION = '1.71.0'
+GRPC_VERSION = grpc.__version__
+_version_not_supported = False
+
+try:
+    from grpc._utilities import first_version_is_lower
+    _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION)
+except ImportError:
+    _version_not_supported = True
+
+if _version_not_supported:
+    raise RuntimeError(
+        f'The grpc package installed is at version {GRPC_VERSION},'
+        + f' but the generated code in async_inference_pb2_grpc.py depends on'
+        + f' grpcio>={GRPC_GENERATED_VERSION}.'
+        + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}'
+        + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.'
+    )
+
+
+class AsyncInferenceStub:
+    """AsyncInference: from Robot perspective
+    Robot send observations to & executes action received from a remote Policy server
+    """
+
+    def __init__(self, channel):
+        """Constructor.
+
+        Args:
+            channel: A grpc.Channel.
+        """
+        self.SendObservations = channel.stream_unary(
+                '/async_inference.AsyncInference/SendObservations',
+                request_serializer=async__inference__pb2.Observation.SerializeToString,
+                response_deserializer=async__inference__pb2.Empty.FromString,
+                _registered_method=True)
+        self.StreamActions = channel.unary_stream(
+                '/async_inference.AsyncInference/StreamActions',
+                request_serializer=async__inference__pb2.Empty.SerializeToString,
+                response_deserializer=async__inference__pb2.Action.FromString,
+                _registered_method=True)
+        self.SendPolicyInstructions = channel.unary_unary(
+                '/async_inference.AsyncInference/SendPolicyInstructions',
+                request_serializer=async__inference__pb2.PolicySetup.SerializeToString,
+                response_deserializer=async__inference__pb2.Empty.FromString,
+                _registered_method=True)
+        self.Ready = channel.unary_unary(
+                '/async_inference.AsyncInference/Ready',
+                request_serializer=async__inference__pb2.Empty.SerializeToString,
+                response_deserializer=async__inference__pb2.Empty.FromString,
+                _registered_method=True)
+
+
+class AsyncInferenceServicer:
+    """AsyncInference: from Robot perspective
+    Robot send observations to & executes action received from a remote Policy server
+    """
+
+    def SendObservations(self, request_iterator, context):
+        """Robot -> Policy to share observations with a remote inference server
+        Policy -> Robot to share actions predicted for given observations
+        """
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def StreamActions(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def SendPolicyInstructions(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Ready(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+
+def add_AsyncInferenceServicer_to_server(servicer, server):
+    rpc_method_handlers = {
+            'SendObservations': grpc.stream_unary_rpc_method_handler(
+                    servicer.SendObservations,
+                    request_deserializer=async__inference__pb2.Observation.FromString,
+                    response_serializer=async__inference__pb2.Empty.SerializeToString,
+            ),
+            'StreamActions': grpc.unary_stream_rpc_method_handler(
+                    servicer.StreamActions,
+                    request_deserializer=async__inference__pb2.Empty.FromString,
+                    response_serializer=async__inference__pb2.Action.SerializeToString,
+            ),
+            'SendPolicyInstructions': grpc.unary_unary_rpc_method_handler(
+                    servicer.SendPolicyInstructions,
+                    request_deserializer=async__inference__pb2.PolicySetup.FromString,
+                    response_serializer=async__inference__pb2.Empty.SerializeToString,
+            ),
+            'Ready': grpc.unary_unary_rpc_method_handler(
+                    servicer.Ready,
+                    request_deserializer=async__inference__pb2.Empty.FromString,
+                    response_serializer=async__inference__pb2.Empty.SerializeToString,
+            ),
+    }
+    generic_handler = grpc.method_handlers_generic_handler(
+            'async_inference.AsyncInference', rpc_method_handlers)
+    server.add_generic_rpc_handlers((generic_handler,))
+    server.add_registered_method_handlers('async_inference.AsyncInference', rpc_method_handlers)
+
+
+ # This class is part of an EXPERIMENTAL API.
+class AsyncInference:
+    """AsyncInference: from Robot perspective
+    Robot send observations to & executes action received from a remote Policy server
+    """
+
+    @staticmethod
+    def SendObservations(request_iterator,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.stream_unary(
+            request_iterator,
+            target,
+            '/async_inference.AsyncInference/SendObservations',
+            async__inference__pb2.Observation.SerializeToString,
+            async__inference__pb2.Empty.FromString,
+            options,
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)
+
+    @staticmethod
+    def StreamActions(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_stream(
+            request,
+            target,
+            '/async_inference.AsyncInference/StreamActions',
+            async__inference__pb2.Empty.SerializeToString,
+            async__inference__pb2.Action.FromString,
+            options,
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)
+
+    @staticmethod
+    def SendPolicyInstructions(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(
+            request,
+            target,
+            '/async_inference.AsyncInference/SendPolicyInstructions',
+            async__inference__pb2.PolicySetup.SerializeToString,
+            async__inference__pb2.Empty.FromString,
+            options,
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)
+
+    @staticmethod
+    def Ready(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(
+            request,
+            target,
+            '/async_inference.AsyncInference/Ready',
+            async__inference__pb2.Empty.SerializeToString,
+            async__inference__pb2.Empty.FromString,
+            options,
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)

lerobot/scripts/server/constants.py

@@ -0,0 +1,12 @@
+"""Server/Client side: Sometimes you just want the environment to wait a tiny bit"""
+
+idle_wait = 0.01
+
+"""Client side: The environment evolves with a time resolution equal to environment_dt"""
+environment_dt = 1 / 30
+
+"""Server side: Running inference on (at most) environment_dt"""
+inference_latency = environment_dt
+
+"""Supported policies"""
+supported_policies = ["act", "smolvla"]

lerobot/scripts/server/helpers.py

@@ -0,0 +1,128 @@
+import logging
+import logging.handlers
+import os
+import time
+from typing import Any
+
+import torch
+
+
+def setup_logging(prefix: str, info_bracket: str):
+    """Sets up logging"""
+    # Create logs directory if it doesn't exist
+    os.makedirs("logs", exist_ok=True)
+
+    # Delete any existing prefix_* log files
+    for old_log_file in os.listdir("logs"):
+        if old_log_file.startswith(prefix) and old_log_file.endswith(".log"):
+            try:
+                os.remove(os.path.join("logs", old_log_file))
+                print(f"Deleted old log file: {old_log_file}")
+            except Exception as e:
+                print(f"Failed to delete old log file {old_log_file}: {e}")
+
+    # Set up logging with both console and file output
+    logger = logging.getLogger(prefix)
+    # Prevent propagation to root logger to avoid duplicate messages
+    logger.propagate = False
+
+    logger.setLevel(logging.INFO)
+
+    # Console handler
+    console_handler = logging.StreamHandler()
+    console_handler.setFormatter(
+        logging.Formatter(
+            f"%(asctime)s.%(msecs)03d [{info_bracket}] [%(levelname)s] %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
+        )
+    )
+    logger.addHandler(console_handler)
+
+    # File handler - creates a new log file for each run
+    file_handler = logging.handlers.RotatingFileHandler(
+        f"logs/policy_server_{int(time.time())}.log",
+        maxBytes=10 * 1024 * 1024,  # 10MB
+        backupCount=5,
+    )
+    file_handler.setFormatter(
+        logging.Formatter(
+            f"%(asctime)s.%(msecs)03d [{info_bracket}] [%(levelname)s] %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
+        )
+    )
+    logger.addHandler(file_handler)
+
+    return logger
+
+
+class TimedData:
+    def __init__(self, timestamp: float, data: Any, timestep: int):
+        """Initialize a TimedData object.
+
+        Args:
+            timestamp: Unix timestamp relative to data's creation.
+            data: The actual data to wrap a timestamp around.
+            timestep: The timestep of the data.
+        """
+        self.timestamp = timestamp
+        self.data = data
+        self.timestep = timestep
+
+    def get_data(self):
+        return self.data
+
+    def get_timestamp(self):
+        return self.timestamp
+
+    def get_timestep(self):
+        return self.timestep
+
+
+class TimedAction(TimedData):
+    def __init__(self, timestamp: float, action: torch.Tensor, timestep: int):
+        super().__init__(timestamp=timestamp, data=action, timestep=timestep)
+
+    def get_action(self):
+        return self.get_data()
+
+
+class TimedObservation(TimedData):
+    def __init__(
+        self,
+        timestamp: float,
+        observation: dict[str, torch.Tensor],
+        timestep: int,
+        transfer_state: int = 0,
+        must_go: bool = False,
+    ):
+        super().__init__(timestamp=timestamp, data=observation, timestep=timestep)
+        self.transfer_state = transfer_state
+        self.must_go = must_go
+
+    def get_observation(self):
+        return self.get_data()
+
+
+class TinyPolicyConfig:
+    def __init__(
+        self,
+        policy_type: str = "act",
+        pretrained_name_or_path: str = "fracapuano/act_so100_test",
+        device: str = "cpu",
+    ):
+        self.policy_type = policy_type
+        self.pretrained_name_or_path = pretrained_name_or_path
+        self.device = device
+
+
+def _compare_observation_states(obs1_state: torch.Tensor, obs2_state: torch.Tensor, atol: float) -> bool:
+    """Check if two observation states are similar, under a tolerance threshold"""
+    return torch.linalg.norm(obs1_state - obs2_state) < atol
+
+
+def observations_similar(obs1: TimedObservation, obs2: TimedObservation, atol: float = 1) -> bool:
+    """Check if two observations are similar, under a tolerance threshold"""
+    obs1_state = obs1.get_observation()["observation.state"]
+    obs2_state = obs2.get_observation()["observation.state"]
+
+    return _compare_observation_states(obs1_state, obs2_state, atol=atol)

lerobot/scripts/server/policy_server.py

@@ -0,0 +1,429 @@
+import itertools
+import pickle  # nosec
+import time
+from concurrent import futures
+from queue import Queue
+from typing import Generator, List, Optional
+
+import async_inference_pb2  # type: ignore
+import async_inference_pb2_grpc  # type: ignore
+import grpc
+import torch
+from datasets import load_dataset
+
+from lerobot.common.policies.factory import get_policy_class
+from lerobot.scripts.server.constants import environment_dt, idle_wait, inference_latency, supported_policies
+from lerobot.scripts.server.helpers import (
+    TimedAction,
+    TimedObservation,
+    TinyPolicyConfig,
+    observations_similar,
+    setup_logging,
+)
+
+
+class PolicyServer(async_inference_pb2_grpc.AsyncInferenceServicer):
+    prefix = "policy_server"
+    info_bracket = "SERVER"
+    logger = setup_logging(prefix, info_bracket)
+
+    def __init__(self):
+        # Initialize dataset action generator (to debug this first version, will be removed in the future)
+        self.action_generator = itertools.cycle(self._stream_action_chunks_from_dataset())
+
+        self._setup_server()
+
+        self.actions_per_chunk = 20
+        self.actions_overlap = 10
+
+        self.running = True
+
+    def _setup_server(self) -> None:
+        """Flushes server state when new client connects."""
+        # only running inference on the latest observation received by the server
+        self.observation_queue = Queue(maxsize=1)
+        self._predicted_timesteps = set()
+        self._predicted_observations = Queue(maxsize=1)
+
+    def Ready(self, request, context):  # noqa: N802
+        client_id = context.peer()
+        self.logger.info(f"Client {client_id} connected and ready")
+        self._setup_server()
+
+        return async_inference_pb2.Empty()
+
+    def SendPolicyInstructions(self, request, context):  # noqa: N802
+        """Receive policy instructions from the robot client"""
+        client_id = context.peer()
+        self.logger.debug(f"Receiving policy instructions from {client_id}")
+
+        policy_specs = pickle.loads(request.data)  # nosec
+        assert isinstance(policy_specs, TinyPolicyConfig), (
+            f"Policy specs must be a TinyPolicyConfig. Got {type(policy_specs)}"
+        )
+
+        self.logger.info(
+            f"Policy type: {policy_specs.policy_type} | "
+            f"Pretrained name or path: {policy_specs.pretrained_name_or_path} | "
+            f"Device: {policy_specs.device}"
+        )
+
+        assert policy_specs.policy_type in supported_policies, (
+            f"Policy type {policy_specs.policy_type} not supported. Supported policies: {supported_policies}"
+        )
+
+        self.device = policy_specs.device
+        self.policy_type = policy_specs.policy_type  # act, pi0, etc.
+
+        policy_class = get_policy_class(self.policy_type)
+
+        start = time.time()
+        self.policy = policy_class.from_pretrained(policy_specs.pretrained_name_or_path)
+        self.policy.to(self.device)
+        end = time.time()
+
+        self.logger.info(f"Time taken to put policy on {self.device}: {end - start:.4f} seconds")
+
+        return async_inference_pb2.Empty()
+
+    def SendObservations(self, request_iterator, context):  # noqa: N802
+        """Receive observations from the robot client"""
+        client_id = context.peer()
+        self.logger.debug(f"Receiving observations from {client_id}")
+
+        for observation in request_iterator:
+            receive_time = time.time()
+            timed_observation = pickle.loads(observation.data)  # nosec
+            deserialize_time = time.time()
+
+            self.logger.debug(f"Received observation #{timed_observation.get_timestep()}")
+
+            if not self._maybe_enqueue_observation(timed_observation):
+                continue
+
+            queue_time = time.time()
+
+            obs_timestep = timed_observation.get_timestep()
+            obs_timestamp = timed_observation.get_timestamp()
+
+            self.logger.info(
+                f"Received observation #{obs_timestep} | "
+                f"Client timestamp: {obs_timestamp:.6f} | "
+                f"Server timestamp: {receive_time:.6f} | "
+            )
+
+            if not hasattr(self, "previous_obs_timestamp"):
+                self.previous_obs_timestamp = obs_timestamp
+
+            self.logger.debug(
+                f"1/DeltaObsT (~frequency): {1 / (1e-6 + obs_timestamp - self.previous_obs_timestamp):.6f} Hz| "
+                f"Network latency: {receive_time - obs_timestamp:.6f}s | "
+                f"Deserialization time: {deserialize_time - receive_time:.6f}s | "
+                f"Queue time: {queue_time - deserialize_time:.6f}s | "
+            )
+
+            self.previous_obs_timestamp = obs_timestamp
+
+        return async_inference_pb2.Empty()
+
+    def StreamActions(self, request, context):  # noqa: N802
+        """Stream actions to the robot client"""
+        client_id = context.peer()
+        self.logger.debug(f"Client {client_id} connected for action streaming")
+
+        # Generate action based on the most recent observation and its timestep
+        try:
+            obs = self.observation_queue.get()
+            self.logger.info(
+                f"Running inference for observation #{obs.get_timestep()} (must_go: {obs.must_go})"
+            )
+
+            if obs:
+                self.last_predicted_obs = obs
+                self._predicted_timesteps.add(obs.get_timestep())
+                start_time = time.time()
+                action_chunk = self._predict_action_chunk(obs)
+                # action_chunk = self._read_action_chunk(obs)
+                inference_time = time.time() - start_time
+
+                start_time = time.time()
+                action_bytes = pickle.dumps(action_chunk)  # nosec
+                serialize_time = time.time() - start_time
+
+                # Create and return the Action
+                action = async_inference_pb2.Action(transfer_state=obs.transfer_state, data=action_bytes)
+
+                self.logger.info(
+                    f"Action chunk #{obs.get_timestep()} generated | Inference time: {inference_time:.6f}s |"
+                )
+
+                self.logger.debug(
+                    f"Action chunk #{obs.get_timestep()} generated | "
+                    f"Inference time: {inference_time:.6f}s |"
+                    f"Serialize time: {serialize_time:.6f}s |"
+                    f"Total time: {inference_time + serialize_time:.6f}s"
+                )
+
+                yield action
+            else:
+                self.logger.warning("No observation in queue yet!")
+                time.sleep(idle_wait)
+
+        except Exception as e:
+            self.logger.error(f"Error in StreamActions: {e}")
+
+        return async_inference_pb2.Empty()
+
+    def _enqueue_and_go(self, obs: TimedObservation):
+        # If queue is full, get the old observation to make room
+        if self.observation_queue.full():
+            # pops from queue
+            _ = self.observation_queue.get_nowait()
+            self.logger.debug("Observation queue was full, removed oldest observation")
+
+        # Now put the new observation (never blocks as queue is non-full here)
+        self.observation_queue.put(obs)
+        return True
+
+    def _obs_sanity_checks(self, obs: TimedObservation, previous_obs: TimedObservation) -> bool:
+        if obs.get_timestep() in self._predicted_timesteps:
+            self.logger.debug(f"Skipping observation #{obs.get_timestep()} - Timestep predicted already!")
+            return False
+
+        elif observations_similar(obs, previous_obs, atol=1):
+            self.logger.debug(
+                f"Skipping observation #{obs.get_timestep()} - Observation too similar to last obs predicted!"
+            )
+            return False
+
+        else:
+            return True
+
+    def _maybe_enqueue_observation(self, obs: TimedObservation) -> bool:
+        """Enqueue an observation if it must go through processing, otherwise skip it.
+        Observations not in queue are never run through the policy network"""
+
+        if obs.must_go or not hasattr(self, "last_predicted_obs"):
+            self.logger.info(f"[MUST GO] Enqueued observation #{obs.get_timestep()} for direct processing!")
+            return self._enqueue_and_go(obs)
+
+        else:
+            if self._obs_sanity_checks(obs, self.last_predicted_obs):
+                return self._enqueue_and_go(obs)
+            else:
+                return False
+
+    def _time_action_chunk(self, t_0: float, action_chunk: list[torch.Tensor], i_0: int) -> list[TimedAction]:
+        """Turn a chunk of actions into a list of TimedAction instances,
+        with the first action corresponding to t_0 and the rest corresponding to
+        t_0 + i*environment_dt for i in range(len(action_chunk))
+        """
+        return [
+            TimedAction(t_0 + i * environment_dt, action, i_0 + i) for i, action in enumerate(action_chunk)
+        ]
+
+    @torch.no_grad()
+    def _run_act_policy(self, observation: dict[str, torch.Tensor]) -> torch.Tensor:
+        """Run ACT-like policies"""
+        start_time = time.time()
+
+        # prepare observation for policy forward pass
+        batch = self.policy.normalize_inputs(observation)
+        normalize_time = time.time()
+        self.logger.debug(f"Observation normalization time: {normalize_time - start_time:.6f}s")
+
+        if self.policy.config.image_features:
+            batch = dict(batch)  # shallow copy so that adding a key doesn't modify the original
+            batch["observation.images"] = [batch[key] for key in self.policy.config.image_features]
+            prep_time = time.time()
+            self.logger.debug(f"Observation image preparation time: {prep_time - normalize_time:.6f}s")
+
+        # forward pass outputs up to policy.config.n_action_steps != actions_per_chunk
+        actions = self.policy.model(batch)[0][:, : self.actions_per_chunk]
+
+        actions = self.policy.unnormalize_outputs({"action": actions})["action"]
+
+        end_time = time.time()
+        self.logger.info(f"[ACT] Action chunk generation total time: {end_time - start_time:.6f}s")
+
+        return actions
+
+    @torch.no_grad()
+    def _run_pi0_policy(self, observation: dict[str, torch.Tensor]) -> torch.Tensor:
+        """Run PI0-like policies"""
+        raise NotImplementedError("PI0 policy not implemented yet")
+
+    @torch.no_grad()
+    def _run_smolvla_policy(
+        self, observation: dict[str, torch.Tensor], noise: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        """Run smolvla-like policies"""
+        observation = self.policy.normalize_inputs(observation)
+
+        images, img_masks = self.policy.prepare_images(observation)
+        state = self.policy.prepare_state(observation)
+        lang_tokens, lang_masks = self.policy.prepare_language(observation)
+
+        actions = self.policy.model.sample_actions(
+            images, img_masks, lang_tokens, lang_masks, state, noise=noise
+        )
+
+        # Unpad actions
+        original_action_dim = self.policy.config.action_feature.shape[0]
+        actions = actions[:, :, :original_action_dim]
+
+        actions = self.policy.unnormalize_outputs(
+            {"action": actions, "robot_type": [self.policy.config.robot_type]}
+        )["action"]
+
+        return actions
+
+    def _get_action_chunk(
+        self, observation: dict[str, torch.Tensor], policy_type: str = "act"
+    ) -> torch.Tensor:
+        """Get an action chunk from the policy"""
+        if policy_type == "act":
+            return self._run_act_policy(observation)
+        elif policy_type == "smolvla":
+            return self._run_smolvla_policy(observation)
+        else:
+            raise ValueError(f"Policy class {policy_type} not supported")
+
+    def _predict_action_chunk(self, observation_t: TimedObservation) -> list[TimedAction]:
+        """Predict an action based on the observation"""
+        """1. Prepare observation"""
+        start_time = time.time()
+
+        observation = {
+            "robot_type": [self.policy.config.robot_type],
+        }
+        for k, v in observation_t.get_observation().items():
+            if isinstance(v, torch.Tensor):  # VLAs present natural-language instructions
+                if "image" in k:
+                    # Add batch dimension first, then reorder to NCHW format, then normalize to [0, 1]
+                    observation[k] = (
+                        v.unsqueeze(0).permute(0, 3, 1, 2).to(self.device, non_blocking=True) / 255.0
+                    )
+                else:
+                    observation[k] = v.unsqueeze(0).to(self.device, non_blocking=True)
+            else:
+                observation[k] = v  # textual instructions are passed as a list of strings
+
+        prep_time = time.time()
+        self.logger.debug(f"Observation preparation time: {prep_time - start_time:.6f}s")
+
+        """2. Get action chunk"""
+        action_tensor = self._get_action_chunk(observation, self.policy_type)
+        action_tensor = action_tensor.squeeze(0)
+
+        # Move to CPU before serializing
+        action_tensor = action_tensor.cpu()
+
+        post_inference_time = time.time()
+        self.logger.debug(f"Post-inference processing start: {post_inference_time - prep_time:.6f}s")
+
+        if action_tensor.dim() == 1:
+            # No chunk dimension, so repeat action to create a (dummy) chunk of actions
+            action_tensor = action_tensor.repeat(self.actions_per_chunk, 1)
+
+        action_chunk = self._time_action_chunk(
+            observation_t.get_timestamp(), list(action_tensor), observation_t.get_timestep()
+        )
+
+        chunk_time = time.time()
+        self.logger.debug(f"Action chunk creation time: {chunk_time - post_inference_time:.6f}s")
+        time.sleep(
+            max(0, inference_latency - max(0, chunk_time - start_time))
+        )  # sleep to control inference latency
+
+        return action_chunk
+
+    def _stream_action_chunks_from_dataset(self) -> Generator[List[torch.Tensor], None, None]:
+        """Stream chunks of actions from a prerecorded dataset.
+
+        Returns:
+            Generator that yields chunks of actions from the dataset
+        """
+        import warnings
+
+        warnings.warn(
+            "This method is deprecated and will be removed in the future.", DeprecationWarning, stacklevel=2
+        )
+
+        dataset = load_dataset("fracapuano/so100_test", split="train").with_format("torch")
+
+        # 1. Select the action column only, where you will find tensors with 6 elements
+        actions = dataset["action"]
+        action_indices = torch.arange(len(actions))
+
+        # 2. Chunk the iterable of tensors into chunks with 10 elements each
+        # sending only first element for debugging
+        indices_chunks = action_indices.unfold(
+            0, self.actions_per_chunk, self.actions_per_chunk - self.actions_overlap
+        )
+
+        for idx_chunk in indices_chunks:
+            yield actions[idx_chunk[0] : idx_chunk[-1] + 1, :]
+
+    def _read_action_chunk(self, observation: Optional[TimedObservation] = None) -> list[TimedAction]:
+        """Dummy function for predicting action chunk given observation.
+
+        Instead of computing actions on-the-fly, this method streams
+        actions from a prerecorded dataset.
+        """
+        import warnings
+
+        warnings.warn(
+            "This method is deprecated and will be removed in the future.", DeprecationWarning, stacklevel=2
+        )
+
+        start_time = time.time()
+        if not observation:
+            observation = TimedObservation(timestamp=time.time(), observation={}, timestep=0)
+
+        # Get chunk of actions from the generator
+        actions_chunk = next(self.action_generator)
+
+        # Return a list of TimedActions, with timestamps starting from the observation timestamp
+        actions_chunk = self._time_action_chunk(
+            observation.get_timestamp(), actions_chunk, observation.get_timestep()
+        )
+
+        chunk_time = time.time()
+        self.logger.debug(f"Action chunk creation time: {chunk_time - start_time:.6f}s")
+
+        # slow action generation, emulates inference time
+        time.sleep(max(0, inference_latency - max(0, chunk_time - start_time)))
+
+        return actions_chunk
+
+    def stop(self):
+        """Stop the server"""
+        self.running = False
+        self.logger.info("Server stopping...")
+
+
+def serve():
+    port = 8080
+    # Create the server instance first
+    policy_server = PolicyServer()
+
+    # Setup and start gRPC server
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+    async_inference_pb2_grpc.add_AsyncInferenceServicer_to_server(policy_server, server)
+    server.add_insecure_port(f"[::]:{port}")
+    server.start()
+    policy_server.logger.info(f"PolicyServer started on port {port}")
+
+    try:
+        # Use the running attribute to control server lifetime
+        while policy_server.running:
+            time.sleep(1)  # Check every second instead of sleeping indefinitely
+
+    except KeyboardInterrupt:
+        policy_server.stop()
+        policy_server.logger.info("Keyboard interrupt received")
+
+
+if __name__ == "__main__":
+    serve()

lerobot/scripts/server/robot_client.py

@@ -0,0 +1,608 @@
+import argparse
+import os
+import pickle  # nosec
+import threading
+import time
+from queue import Empty, Queue
+from typing import Callable, Optional
+
+import async_inference_pb2  # type: ignore
+import async_inference_pb2_grpc  # type: ignore
+import grpc
+import torch
+
+from lerobot.common.robot_devices.robots.utils import make_robot
+from lerobot.scripts.server.constants import environment_dt, idle_wait
+from lerobot.scripts.server.helpers import TimedAction, TimedObservation, TinyPolicyConfig, setup_logging
+
+
+class RobotClient:
+    prefix = "robot_client"
+    info_bracket = "CLIENT"
+    logger = setup_logging(prefix, info_bracket)
+
+    def __init__(
+        self,
+        server_address: Optional[str] = None,
+        policy_type: str = "smolvla",
+        pretrained_name_or_path: str = "lerobot/smolvla_base",
+        policy_device: str = "cuda",
+        chunk_size_threshold: float = 0.5,
+        robot: str = "so100",
+    ):
+        # Use environment variable if server_address is not provided
+        if server_address is None:
+            server_address = os.getenv("SERVER_ADDRESS", "localhost:8080")
+            self.logger.info(f"No server address provided, using default address: {server_address}")
+
+        self.policy_config = TinyPolicyConfig(policy_type, pretrained_name_or_path, policy_device)
+        self.channel = grpc.insecure_channel(server_address)
+        self.stub = async_inference_pb2_grpc.AsyncInferenceStub(self.channel)
+        self.logger.info(f"Initializing client to connect to server at {server_address}")
+
+        self.running = False
+        self.must_go = True  # does the observation qualify for direct processing on the policy server?
+
+        self.latest_action = -1
+        self.action_chunk_size = -1
+
+        self._chunk_size_threshold = chunk_size_threshold
+
+        self.action_queue = Queue()
+        self.start_barrier = threading.Barrier(2)  # 2 threads: action receiver, control loop
+
+        start_time = time.time()
+        self.robot = make_robot(robot)
+        self.robot.connect()
+
+        connect_time = time.time()
+        self.logger.info(f"Robot connection time: {connect_time - start_time:.4f}s")
+
+        time.sleep(idle_wait)  # sleep waiting for cameras to activate
+        self.logger.info("Robot connected and ready")
+
+    def timestamps(self):
+        """Get the timestamps of the actions in the queue"""
+        return sorted([action.get_timestep() for action in self.action_queue.queue])
+
+    def start(self):
+        """Start the robot client and connect to the policy server"""
+        try:
+            # client-server handshake
+            start_time = time.time()
+            self.stub.Ready(async_inference_pb2.Empty())
+            end_time = time.time()
+            self.logger.info(f"Connected to policy server in {end_time - start_time:.4f}s")
+
+            # send policy instructions
+            policy_config_bytes = pickle.dumps(self.policy_config)
+            policy_setup = async_inference_pb2.PolicySetup(
+                transfer_state=async_inference_pb2.TRANSFER_BEGIN, data=policy_config_bytes
+            )
+
+            self.logger.info("Sending policy instructions to policy server")
+            self.logger.info(
+                f"Policy type: {self.policy_config.policy_type} | "
+                f"Pretrained name or path: {self.policy_config.pretrained_name_or_path} | "
+                f"Device: {self.policy_config.device}"
+            )
+
+            self.stub.SendPolicyInstructions(policy_setup)
+
+            self.running = True
+            self.available_actions_size = []
+            return True
+
+        except grpc.RpcError as e:
+            self.logger.error(f"Failed to connect to policy server: {e}")
+            return False
+
+    def stop(self):
+        """Stop the robot client"""
+        self.running = False
+
+        self.robot.disconnect()
+        self.logger.info("Robot disconnected")
+
+        self.channel.close()
+        self.logger.info("Client stopped, channel closed")
+
+    def send_observation(
+        self,
+        obs: TimedObservation,
+        transfer_state: async_inference_pb2.TransferState = async_inference_pb2.TRANSFER_MIDDLE,
+    ) -> bool:
+        """Send observation to the policy server.
+        Returns True if the observation was sent successfully, False otherwise."""
+        if not self.running:
+            self.logger.warning("Client not running")
+            return False
+
+        assert isinstance(obs, TimedObservation), "Input observation needs to be a TimedObservation!"
+
+        start_time = time.time()
+        observation_bytes = pickle.dumps(obs)
+        serialize_time = time.time()
+        self.logger.debug(f"Observation serialization time: {serialize_time - start_time:.6f}s")
+
+        observation = async_inference_pb2.Observation(transfer_state=transfer_state, data=observation_bytes)
+
+        try:
+            send_start = time.time()
+            _ = self.stub.SendObservations(iter([observation]))
+            send_end = time.time()
+
+            obs_timestep = obs.get_timestep()
+
+            self.logger.info(
+                f"Sent observation #{obs_timestep} | "
+                f"Serialize time: {serialize_time - start_time:.6f}s | "
+                f"Network time: {send_end - send_start:.6f}s | "
+                f"Total time: {send_end - start_time:.6f}s"
+            )
+
+            self.last_obs_sent_time = send_end
+            return True
+
+        except grpc.RpcError as e:
+            self.logger.error(f"Error sending observation #{obs.get_timestep()}: {e}")
+            return False
+
+    def _validate_action(self, action: TimedAction):
+        """Received actions are keps only when they have been produced for now or later, never before"""
+        return not action.get_timestep() <= self.latest_action
+
+    def _inspect_action_queue(self):
+        queue_size = self.action_queue.qsize()
+        timestamps = sorted([action.get_timestep() for action in self.action_queue.queue])
+        self.logger.debug(f"Queue size: {queue_size}, Queue contents: {timestamps}")
+        return queue_size, timestamps
+
+    def _update_action_queue(self, actions: list[TimedAction]):
+        """Update the action queue with new actions, without ever emptying the queue"""
+
+        new_queue = Queue()
+        for action in actions:
+            if self._validate_action(action):
+                new_queue.put(action)
+
+        self.action_queue = new_queue
+
+    def _aggregate_action_queues(
+        self,
+        incoming_actions: list[TimedAction],
+        aggregate_fn: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+    ):
+        """Finds the same timestep actions in the queue and aggregates them using the aggregate_fn"""
+        # TODO(fracapuano): move outside of the function and make aggregate_fn an always required argument
+        if not aggregate_fn:
+            # default aggregate function: take the latest action
+            def aggregate_fn(x1, x2):
+                return x2
+
+        action_intersections: list[torch.Tensor] = []
+        current_action_queue = {
+            action.get_timestep(): action.get_action() for action in self.action_queue.queue
+        }
+
+        for new_action in incoming_actions:
+            if new_action.get_timestep() in current_action_queue:
+                # TODO(fracapuano): There is probably a way to do this with broadcasting of the two action tensors
+                action_intersections.append(
+                    TimedAction(
+                        timestamp=new_action.get_timestamp(),
+                        action=aggregate_fn(
+                            current_action_queue[new_action.get_timestep()], new_action.get_action()
+                        ),
+                        timestep=new_action.get_timestep(),
+                    )
+                )
+            else:
+                action_intersections.append(new_action)
+
+        new_queue = Queue()
+        for action in action_intersections:
+            if self._validate_action(action):
+                new_queue.put(action)
+
+        self.action_queue = new_queue
+
+    def _clear_action_queue(self):
+        """Clear the existing queue"""
+        while not self.action_queue.empty():
+            try:
+                self.action_queue.get_nowait()
+            except Empty:
+                break
+
+    def _fill_action_queue(self, actions: list[TimedAction]):
+        """Fill the action queue with incoming valid actions"""
+        start_time = time.time()
+        valid_count = 0
+
+        for action in actions:
+            if self._validate_action(action):
+                self.action_queue.put(action)
+                valid_count += 1
+
+        end_time = time.time()
+        self.logger.debug(
+            f"Queue filled: {valid_count}/{len(actions)} valid actions added in {end_time - start_time:.6f}s"
+        )
+
+    def _clear_and_fill_action_queue(self, actions: list[TimedAction]):
+        self._clear_action_queue()
+        self._fill_action_queue(actions)
+
+    def receive_actions(self):
+        """Receive actions from the policy server"""
+        # Wait at barrier for synchronized start
+        self.start_barrier.wait()
+        self.logger.info("Action receiving thread starting")
+
+        while self.running:
+            try:
+                # Use StreamActions to get a stream of actions from the server
+                for actions_chunk in self.stub.StreamActions(async_inference_pb2.Empty()):
+                    receive_time = time.time()
+
+                    # Deserialize bytes back into list[TimedAction]
+                    deserialize_start = time.time()
+                    timed_actions = pickle.loads(actions_chunk.data)  # nosec
+                    deserialize_end = time.time()
+
+                    self.action_chunk_size = max(self.action_chunk_size, len(timed_actions))
+
+                    start_time = time.time()
+
+                    self.logger.info(f"Current latest action: {self.latest_action}")
+
+                    # Get queue state before changes
+                    old_size, old_timesteps = self._inspect_action_queue()
+                    if not old_timesteps:
+                        old_timesteps = [self.latest_action]  # queue was empty
+
+                    # Log incoming actions
+                    incoming_timesteps = [a.get_timestep() for a in timed_actions]
+
+                    # Calculate network latency if we have matching observations
+                    if len(timed_actions) > 0:
+                        first_action_timestep = timed_actions[0].get_timestep()
+                        server_to_client_latency = receive_time - self.last_obs_sent_time
+
+                        self.logger.info(
+                            f"Received action chunk for step #{first_action_timestep} | "
+                            f"Latest action: #{self.latest_action} | "
+                            f"Network latency (server->client): {server_to_client_latency:.6f}s | "
+                            f"Deserialization time: {deserialize_end - deserialize_start:.6f}s"
+                        )
+
+                    # Update action queue
+                    start_time = time.time()
+                    self._update_action_queue(timed_actions)
+                    queue_update_time = time.time() - start_time
+
+                    self.must_go = (
+                        True  # after receiving actions, next empty queue triggers must-go processing!
+                    )
+
+                    # Get queue state after changes
+                    new_size, new_timesteps = self._inspect_action_queue()
+
+                    self.logger.info(
+                        f"Queue update complete ({queue_update_time:.6f}s) | "
+                        f"Before: {old_size} items | "
+                        f"After: {new_size} items | "
+                    )
+                    self.logger.info(
+                        f"Latest action: {self.latest_action} | "
+                        f"Old action steps: {old_timesteps[0]}:{old_timesteps[-1]} | "
+                        f"Incoming action steps: {incoming_timesteps[0]}:{incoming_timesteps[-1]} | "
+                        f"Updated action steps: {new_timesteps[0]}:{new_timesteps[-1]}"
+                    )
+
+            except grpc.RpcError as e:
+                self.logger.error(f"Error receiving actions: {e}")
+                # Avoid tight loop on action receiver error
+                time.sleep(idle_wait)
+
+    def _actions_available(self):
+        """Check if there are actions available in the queue"""
+        return not self.action_queue.empty()
+
+    def _get_next_action(self) -> Optional[TimedAction]:
+        """Get the next action from the queue"""
+        try:
+            action = self.action_queue.get_nowait()
+            return action
+
+        except Empty:
+            return None
+
+    def _perform_action(self, timed_action: TimedAction):
+        self.robot.send_action(timed_action.get_action())
+        self.latest_action = timed_action.get_timestep()
+
+        self.logger.debug(
+            f"Ts={timed_action.get_timestamp()} | "
+            f"Action #{timed_action.get_timestep()} performed | "
+            f"Queue size: {self.action_queue.qsize()}"
+        )
+
+    def execute_actions(self):
+        """Continuously execute actions from the queue"""
+        import warnings
+
+        warnings.warn("This method is deprecated! Will be removed soon!", stacklevel=2)
+        # Wait at barrier for synchronized start
+        self.start_barrier.wait()
+        time.sleep(idle_wait)  # wait for observation capture to start
+
+        self.logger.info("Action execution thread starting")
+
+        while self.running:
+            # constantly monitor the size of the action queue
+            self.available_actions_size.append(self.action_queue.qsize())
+
+            if self._actions_available():
+                timed_action = self._get_next_action()
+                self._perform_action(timed_action)
+
+                time.sleep(environment_dt)
+
+            else:
+                self.logger.debug("No action available | Sleeping")
+                time.sleep(idle_wait)
+
+    def stream_observations(self, get_observation_fn):
+        """Continuously stream observations to the server"""
+        import warnings
+
+        warnings.warn("This method is deprecated! Will be removed soon!", stacklevel=2)
+
+        # Wait at barrier for synchronized start
+        self.start_barrier.wait()
+        self.logger.info("Observation streaming thread starting")
+
+        while self.running:
+            try:
+                # Get serialized observation bytes from the function
+                start_time = time.time()
+                observation = get_observation_fn()
+                obs_capture_time = time.time() - start_time
+
+                self.logger.debug(f"Capturing observation took {obs_capture_time:.6f}s")
+
+                if not hasattr(self, "last_obs_timestamp"):
+                    self.last_obs_timestamp = observation.get_timestamp()
+
+                obs_timestep, obs_timestamp = observation.get_timestep(), observation.get_timestamp()
+                self.logger.info(
+                    f"Ts={obs_timestamp} | "
+                    f"Captured observation #{obs_timestep} | "
+                    f"1/DeltaTs (~frequency)={1 / (1e-6 + obs_timestamp - self.last_obs_timestamp):.6f}"
+                )
+
+                self.last_obs_timestamp = obs_timestamp
+
+                # Set appropriate transfer state
+                if obs_timestep == 0:
+                    state = async_inference_pb2.TRANSFER_BEGIN
+                else:
+                    state = async_inference_pb2.TRANSFER_MIDDLE
+
+                time.sleep(environment_dt)
+                self.send_observation(observation, state)
+
+            except Exception as e:
+                self.logger.error(f"Error in observation sender: {e}")
+                time.sleep(idle_wait)
+
+    def control_loop_action(self):
+        """Reading and performing actions in local queue"""
+        self.available_actions_size.append(self.action_queue.qsize())
+        if self._actions_available():
+            # Get action from queue
+            get_start = time.time()
+            timed_action = self._get_next_action()
+            get_end = time.time() - get_start
+
+            self.logger.debug(
+                f"Popping action from queue to perform took {get_end:.6f}s | "
+                f"Queue size: {self.action_queue.qsize()}"
+            )
+
+            self._perform_action(timed_action)
+
+    def _ready_to_send_observation(self):
+        """Flags when the client is ready to send an observation"""
+        return self.action_queue.qsize() / self.action_chunk_size <= self._chunk_size_threshold
+
+    def control_loop_observation(self, get_observation_fn):
+        try:
+            # Get serialized observation bytes from the function
+            start_time = time.time()
+            observation = get_observation_fn()
+            obs_capture_time = time.time() - start_time
+
+            # If there are no actions left in the queue, the observation must go through processing!
+            observation.must_go = self.must_go and self.action_queue.empty()
+            self.logger.debug(f"QUEUE SIZE: {self.action_queue.qsize()} (Must go: {observation.must_go})")
+            if observation.must_go:
+                # must-go flag will be set again after receiving actions
+                self.must_go = False
+
+            if not hasattr(self, "last_obs_timestamp"):
+                self.last_obs_timestamp = observation.get_timestamp()
+
+            obs_timestep, obs_timestamp = observation.get_timestep(), observation.get_timestamp()
+            self.last_obs_timestamp = obs_timestamp
+
+            self.logger.info(
+                f"Ts={obs_timestamp} | "
+                f"Captured observation #{obs_timestep} | "
+                f"1/DeltaTs (~frequency)={1 / (1e-6 + obs_timestamp - self.last_obs_timestamp):.6f}"
+            )
+
+            self.logger.debug(f"Capturing observation took {obs_capture_time:.6f}s")
+
+            # Set appropriate transfer state
+            if obs_timestep == 0:
+                state = async_inference_pb2.TRANSFER_BEGIN
+            else:
+                state = async_inference_pb2.TRANSFER_MIDDLE
+
+            self.send_observation(observation, state)
+
+        except Exception as e:
+            self.logger.error(f"Error in observation sender: {e}")
+
+    def control_loop(self, get_observation_fn):
+        """Combined function for executing actions and streaming observations"""
+        # Wait at barrier for synchronized start
+        self.start_barrier.wait()
+        self.logger.info("Control loop thread starting")
+
+        control_loops = 0
+        while self.running:
+            control_loop_start = time.time()
+            self.control_loop_action()
+
+            """Control loop: (2) Streaming observations to the remote policy server"""
+            if self._ready_to_send_observation() or control_loops == 0:
+                self.control_loop_observation(get_observation_fn)
+
+            # Dynamically adjust sleep time to maintain the desired control frequency
+            time.sleep(max(0, environment_dt - (time.time() - control_loop_start)))
+            control_loops += 1
+
+
+def async_client(task_instruction: str, verbose: int = 0):
+    client = RobotClient()
+
+    if client.start():
+        # Function to get observations from the robot
+        def get_observation():
+            observation_content = None
+            observation_content = client.robot.capture_observation()
+
+            observation_content["task"] = [task_instruction]
+
+            observation = TimedObservation(
+                timestamp=time.time(), observation=observation_content, timestep=max(client.latest_action, 0)
+            )
+
+            return observation
+
+        client.logger.info("Starting all threads...")
+
+        # Create and start action receiver thread
+        action_receiver_thread = threading.Thread(target=client.receive_actions)
+        action_receiver_thread.daemon = True
+
+        control_loop_thread = threading.Thread(target=client.control_loop, args=(get_observation,))
+        control_loop_thread.daemon = True
+
+        # Start all threads
+        action_receiver_thread.start()
+        control_loop_thread.start()
+
+        try:
+            while client.running:
+                time.sleep(idle_wait)
+
+        except KeyboardInterrupt:
+            pass
+
+        finally:
+            client.stop()
+            client.logger.info("Client stopped")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Robot client for executing tasks via policy server")
+    parser.add_argument(
+        "--task",
+        type=str,
+        required=True,
+        help="Task instruction for the robot to execute (e.g., 'fold my tshirt')",
+    )
+    parser.add_argument("--verbose", type=int, default=0, help="Verbosity level (default: 0)")
+    parser.add_argument(
+        "--server-port-address",
+        type=str,
+        default="localhost:8080",
+        help="Server & port address (default: localhost:8080, or SERVER_ADDRESS env var)",
+    )
+    parser.add_argument("--policy-type", type=str, default="smolvla", help="Policy type (default: smolvla)")
+    parser.add_argument(
+        "--pretrained-name-or-path",
+        type=str,
+        default="lerobot/smolvla_base",
+        help="Pretrained model name or path (default: lerobot/smolvla_base)",
+    )
+    parser.add_argument(
+        "--policy-device", type=str, default="cuda", help="Device for policy inference (default: cuda)"
+    )
+    parser.add_argument(
+        "--chunk-size-threshold",
+        type=float,
+        default=0.5,
+        help="Chunk size threshold (`g` in the paper, default: 0.5)",
+    )
+    parser.add_argument(
+        "--robot",
+        type=str,
+        default="so100",
+        help="Robot name, as per the `make_robot` function (default: so100)",
+    )
+
+    args = parser.parse_args()
+
+    # Create client with parsed arguments
+    client = RobotClient(
+        server_address=args.server_address,
+        policy_type=args.policy_type,
+        pretrained_name_or_path=args.pretrained_name_or_path,
+        policy_device=args.policy_device,
+        chunk_size_threshold=args.chunk_size_threshold,
+        robot=args.robot,
+    )
+
+    if client.start():
+        # Function to get observations from the robot
+        def get_observation():
+            observation_content = None
+            observation_content = client.robot.capture_observation()
+
+            observation_content["task"] = [args.task]
+
+            observation = TimedObservation(
+                timestamp=time.time(), observation=observation_content, timestep=max(client.latest_action, 0)
+            )
+
+            return observation
+
+        client.logger.info("Starting all threads...")
+
+        # Create and start action receiver thread
+        action_receiver_thread = threading.Thread(target=client.receive_actions)
+        action_receiver_thread.daemon = True
+
+        control_loop_thread = threading.Thread(target=client.control_loop, args=(get_observation,))
+        control_loop_thread.daemon = True
+
+        # Start all threads
+        action_receiver_thread.start()
+        control_loop_thread.start()
+
+        try:
+            while client.running:
+                time.sleep(idle_wait)
+
+        except KeyboardInterrupt:
+            pass
+
+        finally:
+            client.stop()
+            client.logger.info("Client stopped")

pyproject.toml

@@ -68,8 +68,8 @@ dependencies = [
     "pyzmq>=26.2.1",
     "rerun-sdk>=0.21.0",
     "termcolor>=2.4.0",
-    "torch>=2.2.1,<2.7",
-    "torchcodec==0.2.1; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
+    "torch>=2.2.1",
+    "torchcodec>=0.2.1; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
     "torchvision>=0.21.0",
     "wandb>=0.16.3",
     "zarr>=2.17.0",

tests/artifacts/image_transforms/default_transforms.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0389a716d51c1c615fb2a3bfa386d89f00b0deca08c4fa21b23e020a939d0213
+oid sha256:6b1e600768a8771c5fe650e038a1193597e3810f032041b2a0d021e4496381c1
 size 3686488

tests/artifacts/image_transforms/save_image_transforms_to_safetensors.py

@@ -28,7 +28,7 @@ from lerobot.common.datasets.transforms import (
 from lerobot.common.utils.random_utils import seeded_context
 
 ARTIFACT_DIR = Path("tests/artifacts/image_transforms")
-DATASET_REPO_ID = "lerobot/aloha_mobile_shrimp"
+DATASET_REPO_ID = "lerobot/aloha_static_cups_open"
 
 
 def save_default_config_transform(original_frame: torch.Tensor, output_dir: Path):

tests/artifacts/image_transforms/single_transforms.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0dc691503e7d90b2086bb408e89a65f772ce5ee6e3562ef8c127bcb09bd90851
+oid sha256:9d4ebab73eabddc58879a4e770289d19e00a1a4cf2fa5fa33cd3a3246992bc90
 size 40551392

tests/artifacts/policies/aloha_sim_insertion_human_act_/actions.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cc67af1d60f95d84c98d6c9ebd648990e0f0705368bd6b72d2b39533950b0179
+oid sha256:f3e4c8e85e146b043fd4e4984947c2a6f01627f174a19f18b5914cf690579d77
 size 5104

tests/artifacts/policies/aloha_sim_insertion_human_act_/grad_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:64518cf652105d15f5fd2cfc13d0681f66a4ec4797dc5d5dc2f7b0d91fe5dfd6
+oid sha256:1a7a8b1a457149109f843c32bcbb047d09de2201847b9b79f7501b447f77ecf4
 size 31672

tests/artifacts/policies/aloha_sim_insertion_human_act_/output_dict.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:32b6d14fab4244b5140adb345e47f662b6739c04974e04b21c3127caa988abbb
+oid sha256:5e6ce85296b2009e7c2060d336c0429b1c7197d9adb159e7df0ba18003067b36
 size 68

tests/artifacts/policies/aloha_sim_insertion_human_act_/param_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e1904ef0338f7b6efdec70ec235ee931b5751008bf4eb433edb0b3fa0838a4f1
+oid sha256:9b5f557e30aead3731c38cbd85af8c706395d8689a918ad88805b5a886245603
 size 33400

tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/actions.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fa544a97f00bf46393a09b006b44c2499bbf7d177782360a8c21cacbf200c07a
+oid sha256:2e6625cabfeb4800abc80252cf9112a9271c154edd01eb291658f143c951610b
 size 515400

tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/grad_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:83c7a8ae912300b5cedba31904f7ba22542059fd60dd86548a95e415713f719e
+oid sha256:224b5fa4828aa88171b68c036e8919c1eae563e2113f03b6461eadf5bf8525a6
 size 31672

tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/output_dict.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5a010633237b3a1141603c65174c551daa9e7b4c474af5a1376d73e5425bfb5d
+oid sha256:016d2fa8fe5f58017dfd46f4632fdc19dfd751e32a2c7cde2077c6f95546d6bd
 size 68

tests/artifacts/policies/aloha_sim_insertion_human_act_1000_steps/param_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ec8b5c440e9fcec190c9be48b28ebb79f82ae63626afe7c811e4bb0c3dd08842
+oid sha256:021562ee3e4814425e367ed0c144d6fbe2eb28838247085716cf0b58fd69a075
 size 33400

tests/datasets/test_image_transforms.py

@@ -16,6 +16,7 @@
 
 import pytest
 import torch
+from packaging import version
 from safetensors.torch import load_file
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import functional as F  # noqa: N812
@@ -253,7 +254,14 @@ def test_backward_compatibility_single_transforms(
 
 
 @require_x86_64_kernel
+@pytest.mark.skipif(
+    version.parse(torch.__version__) < version.parse("2.7.0"),
+    reason="Test artifacts were generated with PyTorch >= 2.7.0 which has different multinomial behavior",
+)
 def test_backward_compatibility_default_config(img_tensor, default_transforms):
+    # NOTE: PyTorch versions have different randomness, it might break this test.
+    # See this PR: https://github.com/huggingface/lerobot/pull/1127.
+
     cfg = ImageTransformsConfig(enable=True)
     default_tf = ImageTransforms(cfg)
 

tests/optim/test_schedulers.py

@@ -37,7 +37,6 @@ def test_diffuser_scheduler(optimizer):
         "base_lrs": [0.001],
         "last_epoch": 1,
         "lr_lambdas": [None],
-        "verbose": False,
     }
     assert scheduler.state_dict() == expected_state_dict
 
@@ -56,7 +55,6 @@ def test_vqbet_scheduler(optimizer):
         "base_lrs": [0.001],
         "last_epoch": 1,
         "lr_lambdas": [None],
-        "verbose": False,
     }
     assert scheduler.state_dict() == expected_state_dict
 
@@ -77,7 +75,6 @@ def test_cosine_decay_with_warmup_scheduler(optimizer):
         "base_lrs": [0.001],
         "last_epoch": 1,
         "lr_lambdas": [None],
-        "verbose": False,
     }
     assert scheduler.state_dict() == expected_state_dict
 

tests/policies/test_policies.py

@@ -20,6 +20,7 @@ from pathlib import Path
 import einops
 import pytest
 import torch
+from packaging import version
 from safetensors.torch import load_file
 
 from lerobot import available_policies
@@ -408,7 +409,16 @@ def test_backward_compatibility(ds_repo_id: str, policy_name: str, policy_kwargs
         4. Check that this test now passes.
         5. Remember to restore `tests/scripts/save_policy_to_safetensors.py` to its original state.
         6. Remember to stage and commit the resulting changes to `tests/artifacts`.
+
+    NOTE: If the test does not pass, and you don't change the policy, it is likely that the test artifact
+    is out of date. For example, some PyTorch versions have different randomness, see this PR:
+    https://github.com/huggingface/lerobot/pull/1127.
+
     """
+    # NOTE: ACT policy has different randomness, after PyTorch 2.7.0
+    if policy_name == "act" and version.parse(torch.__version__) < version.parse("2.7.0"):
+        pytest.skip(f"Skipping act policy test with PyTorch {torch.__version__}. Requires PyTorch >= 2.7.0")
+
     ds_name = ds_repo_id.split("/")[-1]
     artifact_dir = Path("tests/artifacts/policies") / f"{ds_name}_{policy_name}_{file_name_extra}"
     saved_output_dict = load_file(artifact_dir / "output_dict.safetensors")