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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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23
lerobot/scripts/server/actor_server.py
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@@ -28,7 +28,6 @@ from torch.multiprocessing import Event, Queue
# TODO: Remove the import of maniskill
from lerobot.common.policies.factory import make_policy
from lerobot.common.policies.sac.modeling_sac import SACPolicy
from lerobot.common.robot_devices.robots.utils import Robot, make_robot
from lerobot.common.robot_devices.utils import busy_wait
from lerobot.common.utils.random_utils import set_seed
from lerobot.common.utils.utils import (
@@ -268,7 +267,7 @@ def update_policy_parameters(policy: SACPolicy, parameters_queue: Queue, device)
def act_with_policy(
cfg: TrainPipelineConfig,
robot: Robot,
# robot: Robot,
reward_classifier: nn.Module,
shutdown_event: any, # Event,
parameters_queue: Queue,
@@ -287,7 +286,7 @@ def act_with_policy(
logging.info("make_env online")
online_env = make_robot_env( cfg=cfg.env)
online_env = make_robot_env(cfg=cfg.env)
set_seed(cfg.seed)
device = get_safe_torch_device(cfg.policy.device, log=True)
@@ -503,7 +502,6 @@ def actor_cli(cfg: TrainPipelineConfig):
mp.set_start_method("spawn")
init_logging(log_file="actor.log")
robot = make_robot(robot_type=cfg.env.robot)
shutdown_event = setup_process_handlers(use_threads(cfg))
@@ -563,18 +561,17 @@ def actor_cli(cfg: TrainPipelineConfig):
# HACK: FOR MANISKILL we do not have a reward classifier
# TODO: Remove this once we merge into main
reward_classifier = None
if (
cfg.env.reward_classifier["pretrained_path"] is not None
and cfg.env.reward_classifier["config_path"] is not None
):
reward_classifier = get_classifier(
pretrained_path=cfg.env.reward_classifier["pretrained_path"],
config_path=cfg.env.reward_classifier["config_path"],
)
# if (
# cfg.env.reward_classifier["pretrained_path"] is not None
# and cfg.env.reward_classifier["config_path"] is not None
# ):
# reward_classifier = get_classifier(
# pretrained_path=cfg.env.reward_classifier["pretrained_path"],
# config_path=cfg.env.reward_classifier["config_path"],
# )
act_with_policy(
cfg=cfg,
robot=robot,
reward_classifier=reward_classifier,
shutdown_event=shutdown_event,
parameters_queue=parameters_queue,