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docs/source/_toctree.yml

@@ -10,8 +10,3 @@
   - local: getting_started_real_world_robot
     title: Getting Started with Real-World Robots
   title: "Tutorials"
-- sections:
-  - local: smolvla
-    title: Use SmolVLA
-  title: "Policies"
-

docs/source/smolvla.mdx

@@ -1,91 +0,0 @@
-# Use SmolVLA
-
-SmolVLA is designed to be easy to use and integrate—whether you're finetuning on your own data or plugging it into an existing robotics stack.
-
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/aooU0a3DMtYmy_1IWMaIM.png" alt="SmolVLA architecture." width="500"/>
-  <br/>
-  <em>Figure 2. SmolVLA takes as input a sequence of RGB images from multiple cameras, the robot’s current sensorimotor state, and a natural language instruction. The VLM encodes these into contextual features, which condition the action expert to generate a continuous sequence of actions.</em>
-</p>
-
-###  Install
-
-First, install the required dependencies:
-
-```python
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-pip install -e ".[smolvla]"
-```
-
-### Finetune the pretrained model
-Use [`smolvla_base`](https://hf.co/lerobot/smolvla_base), our pretrained 450M model, with the lerobot training framework:
-
-```python
-python lerobot/scripts/train.py \
-  --policy.path=lerobot/smolvla_base \
-  --dataset.repo_id=lerobot/svla_so100_stacking \
-  --batch_size=64 \
-  --steps=200000
-```
-
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/640e21ef3c82bd463ee5a76d/S-3vvVCulChREwHDkquoc.gif" alt="Comparison of SmolVLA across task variations." width="500"/>
-  <br/>
-  <em>Figure 1: Comparison of SmolVLA across task variations. From left to right: (1) asynchronous pick-place cube counting, (2) synchronous pick-place cube counting, (3) pick-place cube counting under perturbations, and (4) generalization on pick-and-place of the lego block with real-world SO101.</em>
-</p>
-
-
-### Train from scratch
-
-​​If you'd like to build from the architecture (pretrained VLM + action expert) rather than a pretrained checkpoint:
-
-```python
-python lerobot/scripts/train.py \
-  --policy.type=smolvla \
-  --dataset.repo_id=lerobot/svla_so100_stacking \
-  --batch_size=64 \
-  --steps=200000
-```
-You can also load `SmolVLAPolicy` directly:
-
-```python
-from lerobot.common.policies.smolvla.modeling_smolvla import SmolVLAPolicy
-policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base")
-```
-
-## Evaluate the pretrained policy and run it in real-time
-
-If you want to record the evaluation process and safe the videos on the hub, login to your HF account by running:
-
-```python
-huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
-```
-
-Store your Hugging Face repository name in a variable to run these commands:
-
-```python
-HF_USER=$(huggingface-cli whoami | head -n 1)
-echo $HF_USER
-```
-Now, indicate the path to the policy, which is `lerobot/smolvla_base` in this case, and run:
-
-```python
-
-python lerobot/scripts/control_robot.py \
-  --robot.type=so100 \
-  --control.type=record \
-  --control.fps=30 \
-  --control.single_task="Grasp a lego block and put it in the bin." \
-  --control.repo_id=${HF_USER}/eval_svla_base_test \
-  --control.tags='["tutorial"]' \
-  --control.warmup_time_s=5 \
-  --control.episode_time_s=30 \
-  --control.reset_time_s=30 \
-  --control.num_episodes=10 \
-  --control.push_to_hub=true \
-  --control.policy.path=lerobot/smolvla_base
-
-```
-
-Depending on your evaluation setup, you can configure the duration and the number of episodes to record for your evaluation suite.

lerobot/__init__.py

@@ -168,7 +168,7 @@ available_datasets = sorted(
 )
 
 # lists all available policies from `lerobot/common/policies`
-available_policies = ["act", "diffusion", "tdmpc", "vqbet"]
+available_policies = ["act", "diffusion", "tdmpc", "vqbet", "smolvla"]
 
 # lists all available robots from `lerobot/common/robot_devices/robots`
 available_robots = [

lerobot/common/policies/smolvla/modeling_smolvla.py

@@ -662,6 +662,7 @@ class VLAFlowMatching(nn.Module):
             self.config.max_period,
             device=device,
         )
+
         time_emb = time_emb.type(dtype=dtype)
 
         time_emb = time_emb[:, None, :].expand_as(action_emb)

lerobot/common/robot_devices/control_utils.py

@@ -272,7 +272,6 @@ def control_loop(
                 action = {"action": action}
 
         if dataset is not None:
-            observation = {k: v for k, v in observation.items() if k not in ["task", "robot_type"]}
             frame = {**observation, **action, "task": single_task}
             dataset.add_frame(frame)
 

tests/test_available.py

@@ -21,6 +21,7 @@ import pytest
 import lerobot
 from lerobot.common.policies.act.modeling_act import ACTPolicy
 from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
+from lerobot.common.policies.smolvla.modeling_smolvla import SmolVLAPolicy
 from lerobot.common.policies.tdmpc.modeling_tdmpc import TDMPCPolicy
 from lerobot.common.policies.vqbet.modeling_vqbet import VQBeTPolicy
 from tests.utils import require_env
@@ -45,7 +46,7 @@ def test_available_policies():
     This test verifies that the class attribute `name` for all policies is
     consistent with those listed in `lerobot/__init__.py`.
     """
-    policy_classes = [ACTPolicy, DiffusionPolicy, TDMPCPolicy, VQBeTPolicy]
+    policy_classes = [ACTPolicy, DiffusionPolicy, TDMPCPolicy, VQBeTPolicy, SmolVLAPolicy]
     policies = [pol_cls.name for pol_cls in policy_classes]
     assert set(policies) == set(lerobot.available_policies), policies