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Add end effector action space to hil-serl (#861)

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Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Michel Aractingi
2025-03-17 14:22:33 +01:00
parent 66816fd871
commit 7b01e16439
13 changed files with 2138 additions and 139 deletions
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18
lerobot/scripts/server/actor_server.py
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@@ -54,6 +54,7 @@ from lerobot.scripts.server.network_utils import (
)
from lerobot.scripts.server.gym_manipulator import get_classifier, make_robot_env
from lerobot.scripts.server import learner_service
from lerobot.common.robot_devices.utils import busy_wait
from torch.multiprocessing import Queue, Event
from queue import Empty
@@ -312,17 +313,6 @@ def act_with_policy(
logging.info("make_policy")
# HACK: This is an ugly hack to pass the normalization parameters to the policy
# Because the action space is dynamic so we override the output normalization parameters
# it's ugly, we know ... and we will fix it
min_action_space: list = online_env.action_space.spaces[0].low.tolist()
max_action_space: list = online_env.action_space.spaces[0].high.tolist()
output_normalization_params: dict[dict[str, list]] = {
"action": {"min": min_action_space, "max": max_action_space}
}
cfg.policy.output_normalization_params = output_normalization_params
cfg.policy.output_shapes["action"] = online_env.action_space.spaces[0].shape
### Instantiate the policy in both the actor and learner processes
### To avoid sending a SACPolicy object through the port, we create a policy intance
### on both sides, the learner sends the updated parameters every n steps to update the actor's parameters
@@ -347,6 +337,7 @@ def act_with_policy(
episode_intervention = False
for interaction_step in range(cfg.training.online_steps):
start_time = time.perf_counter()
if shutdown_event.is_set():
logging.info("[ACTOR] Shutting down act_with_policy")
return
@@ -408,7 +399,6 @@ def act_with_policy(
complementary_info=info, # TODO Handle information for the transition, is_demonstraction: bool
)
)
# assign obs to the next obs and continue the rollout
obs = next_obs
@@ -449,6 +439,10 @@ def act_with_policy(
episode_intervention = False
obs, info = online_env.reset()
if cfg.fps is not None:
dt_time = time.perf_counter() - start_time
busy_wait(1 / cfg.fps - dt_time)
def push_transitions_to_transport_queue(transitions: list, transitions_queue):
"""Send transitions to learner in smaller chunks to avoid network issues.