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Added gripper control mechanism to gym_manipulator

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Moved HilSerl env config to configs/env/configs.py
fixes in actor_server and modeling_sac and configuration_sac
added the possibility of ignoring missing keys in env_cfg in get_features_from_env_config function
This commit is contained in:
Michel Aractingi
2025-03-28 08:21:36 +01:00
committed by AdilZouitine
parent 79e0f6e06c
commit 02b9ea9446
7 changed files with 179 additions and 130 deletions
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58
lerobot/common/policies/sac/configuration_sac.py
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@@ -20,7 +20,7 @@ from typing import Any, Optional
from lerobot.common.optim.optimizers import MultiAdamConfig
from lerobot.configs.policies import PreTrainedConfig
from lerobot.configs.types import NormalizationMode, PolicyFeature, FeatureType
from lerobot.configs.types import NormalizationMode, PolicyFeature, FeatureType
@dataclass
@@ -29,7 +29,6 @@ class ConcurrencyConfig:
learner: str = "threads"
@dataclass
class ActorLearnerConfig:
learner_host: str = "127.0.0.1"
@@ -110,6 +109,7 @@ class SACConfig(PreTrainedConfig):
use_backup_entropy: Whether to use backup entropy for the SAC algorithm.
grad_clip_norm: Gradient clipping norm for the SAC algorithm.
"""
normalization_mapping: dict[str, NormalizationMode] = field(
default_factory=lambda: {
"VISUAL": NormalizationMode.MEAN_STD,
@@ -152,8 +152,8 @@ class SACConfig(PreTrainedConfig):
camera_number: int = 1
device: str = "cuda"
storage_device: str = "cpu"
# Set to "helper2424/resnet10" for hil serl
vision_encoder_name: str | None = None
# Set to "helper2424/resnet10" for hil serl
vision_encoder_name: str | None = None
freeze_vision_encoder: bool = True
image_encoder_hidden_dim: int = 32
shared_encoder: bool = True
@@ -163,7 +163,7 @@ class SACConfig(PreTrainedConfig):
online_env_seed: int = 10000
online_buffer_capacity: int = 100000
offline_buffer_capacity: int = 100000
online_step_before_learning: int = 100
online_step_before_learning: int = 100
policy_update_freq: int = 1
# SAC algorithm parameters
@@ -181,24 +181,14 @@ class SACConfig(PreTrainedConfig):
target_entropy: float | None = None
use_backup_entropy: bool = True
grad_clip_norm: float = 40.0
# Network configuration
critic_network_kwargs: CriticNetworkConfig = field(
default_factory=CriticNetworkConfig
)
actor_network_kwargs: ActorNetworkConfig = field(
default_factory=ActorNetworkConfig
)
policy_kwargs: PolicyConfig = field(
default_factory=PolicyConfig
)
actor_learner_config: ActorLearnerConfig = field(
default_factory=ActorLearnerConfig
)
concurrency: ConcurrencyConfig = field(
default_factory=ConcurrencyConfig
)
critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
actor_network_kwargs: ActorNetworkConfig = field(default_factory=ActorNetworkConfig)
policy_kwargs: PolicyConfig = field(default_factory=PolicyConfig)
actor_learner_config: ActorLearnerConfig = field(default_factory=ActorLearnerConfig)
concurrency: ConcurrencyConfig = field(default_factory=ConcurrencyConfig)
def __post_init__(self):
super().__post_init__()
@@ -218,18 +208,20 @@ class SACConfig(PreTrainedConfig):
return None
def validate_features(self) -> None:
if "observation.image" not in self.input_features:
raise ValueError("You must provide 'observation.image' in the input features")
if "observation.state" not in self.input_features:
raise ValueError("You must provide 'observation.state' in the input features")
has_image = any(key.startswith("observation.image") for key in self.input_features)
has_state = "observation.state" in self.input_features
if not (has_state or has_image):
raise ValueError(
"You must provide either 'observation.state' or an image observation (key starting with 'observation.image') in the input features"
)
if "action" not in self.output_features:
raise ValueError("You must provide 'action' in the output features")
@property
def image_features(self) -> list[str]:
return [key for key in self.input_features.keys() if 'image' in key]
return [key for key in self.input_features.keys() if "image" in key]
@property
def observation_delta_indices(self) -> list:
@@ -243,9 +235,13 @@ class SACConfig(PreTrainedConfig):
def reward_delta_indices(self) -> None:
return None
if __name__ == "__main__":
import draccus
config = SACConfig()
draccus.set_config_type("json")
draccus.dump(config=config, stream=open(file='run_config.json', mode='w'), )
draccus.dump(
config=config,
stream=open(file="run_config.json", mode="w"),
)