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lerobot
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Added logging for interventions to monitor the rate of interventions through time 

Added an s keyboard command to force success in the case the reward classifier fails

Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
This commit is contained in:
Michel Aractingi 2025-02-13 11:04:49 +01:00
parent b9217b06db
commit dc086dc21f
5 changed files with 45 additions and 18 deletions
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8
lerobot/configs/env/so100_real.yaml vendored
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@ -12,10 +12,10 @@ env:
wrapper:
crop_params_dict:
observation.images.front: [126, 43, 329, 518]
observation.images.side: [93, 69, 381, 434]
# observation.images.front: [135, 59, 331, 527]
# observation.images.side: [79, 47, 397, 450]
observation.images.front: [102, 43, 358, 523]
observation.images.side: [92, 123, 379, 349]
# observation.images.front: [109, 37, 361, 557]
# observation.images.side: [94, 161, 372, 315]
resize_size: [128, 128]
control_time_s: 20
reset_follower_pos: true

12
lerobot/configs/policy/hilserl_classifier.yaml
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@ -4,8 +4,9 @@ defaults:
- _self_
seed: 13
dataset_repo_id: aractingi/push_cube_square_reward_cropped_resized
dataset_root: data/aractingi/push_cube_square_reward_cropped_resized
dataset_repo_id: aractingi/push_cube_square_light_reward_cropped_resized
# aractingi/push_cube_square_reward_1_cropped_resized
dataset_root: data/aractingi/push_cube_square_light_reward_cropped_resized
local_files_only: true
train_split_proportion: 0.8
@ -26,7 +27,6 @@ training:
eval_freq: 1 # How often to run validation (in epochs)
save_freq: 1 # How often to save checkpoints (in epochs)
save_checkpoint: true
# image_keys: ["observation.images.top", "observation.images.wrist"]
image_keys: ["observation.images.front", "observation.images.side"]
label_key: "next.reward"
profile_inference_time: false
@ -37,8 +37,8 @@ eval:
num_samples_to_log: 30 # Number of validation samples to log in the table
policy:
name: "hilserl/classifier/push_cube_square_reward_cropped_resized" #"hilserl/classifier/pick_place_lego_cube_120
model_name: "helper2424/resnet10" # "facebook/convnext-base-224" #"helper2424/resnet10"
name: "hilserl/classifier"
model_name: "helper2424/resnet10" # "facebook/convnext-base-224
model_type: "cnn"
num_cameras: 2 # Has to be len(training.image_keys)
@ -50,4 +50,4 @@ wandb:
device: "mps"
resume: false
output_dir: "outputs/classifier/resnet10_frozen"
output_dir: "outputs/classifier/old_trainer_resnet10_frozen"

4
lerobot/scripts/server/actor_server.py
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@ -223,6 +223,7 @@ def act_with_policy(cfg: DictConfig, robot: Robot, reward_classifier: nn.Module)
sum_reward_episode = 0
list_transition_to_send_to_learner = []
list_policy_time = []
episode_intervention = False
for interaction_step in range(cfg.training.online_steps):
if interaction_step >= cfg.training.online_step_before_learning:
@ -252,6 +253,7 @@ def act_with_policy(cfg: DictConfig, robot: Robot, reward_classifier: nn.Module)
if info["is_intervention"]:
# TODO: Check the shape
action = info["action_intervention"]
episode_intervention = True
# Check for NaN values in observations
for key, tensor in obs.items():
@ -295,11 +297,13 @@ def act_with_policy(cfg: DictConfig, robot: Robot, reward_classifier: nn.Module)
interaction_message={
"Episodic reward": sum_reward_episode,
"Interaction step": interaction_step,
"Episode intervention": int(episode_intervention),
**stats,
}
)
)
sum_reward_episode = 0.0
episode_intervention = False
obs, info = online_env.reset()

14
lerobot/scripts/server/crop_dataset_roi.py
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@ -245,10 +245,18 @@ if __name__ == "__main__":
images = {k: v.cpu().permute(1, 2, 0).numpy() for k, v in images.items()}
images = {k: (v * 255).astype("uint8") for k, v in images.items()}
rois = select_square_roi_for_images(images)
# rois = select_square_roi_for_images(images)
rois = {
"observation.images.front": [102, 43, 358, 523],
"observation.images.side": [92, 123, 379, 349],
}
# rois = {
# "observation.images.front": [126, 43, 329, 518],
# "observation.images.side": [93, 69, 381, 434],
# "observation.images.side": (92, 123, 379, 349),
# "observation.images.front": (109, 37, 361, 557),
# }
# rois = {
# "observation.images.front": [109, 37, 361, 557],
# "observation.images.side": [94, 161, 372, 315],
# }
# Print the selected rectangular ROIs

25
lerobot/scripts/server/gym_manipulator.py
View File

@ -312,7 +312,7 @@ class RewardWrapper(gym.Wrapper):
start_time = time.perf_counter()
with torch.inference_mode():
reward = (
self.reward_classifier.predict_reward(images, threshold=0.5)
self.reward_classifier.predict_reward(images, threshold=0.6)
if self.reward_classifier is not None
else 0.0
)
@ -507,6 +507,7 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
"pause_policy": False,
"reset_env": False,
"human_intervention_step": False,
"episode_success": False,
}
self.event_lock = Lock() # Thread-safe access to events
self._init_keyboard_listener()
@ -528,7 +529,12 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
if key == keyboard.Key.right or key == keyboard.Key.esc:
print("Right arrow key pressed. Exiting loop...")
self.events["exit_early"] = True
elif key == keyboard.Key.space and not self.events["exit_early"]:
return
if hasattr(key, "char") and key.char == "s":
print("Key 's' pressed. Episode success triggered.")
self.events["episode_success"] = True
return
if key == keyboard.Key.space and not self.events["exit_early"]:
if not self.events["pause_policy"]:
print(
"Space key pressed. Human intervention required.\n"
@ -536,15 +542,18 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
)
self.events["pause_policy"] = True
log_say("Human intervention stage. Get ready to take over.", play_sounds=True)
elif self.events["pause_policy"] and not self.events["human_intervention_step"]:
return
if self.events["pause_policy"] and not self.events["human_intervention_step"]:
self.events["human_intervention_step"] = True
print("Space key pressed. Human intervention starting.")
log_say("Starting human intervention.", play_sounds=True)
else:
return
if self.events["pause_policy"] and self.events["human_intervention_step"]:
self.events["pause_policy"] = False
self.events["human_intervention_step"] = False
print("Space key pressed for a third time.")
log_say("Continuing with policy actions.", play_sounds=True)
return
except Exception as e:
print(f"Error handling key press: {e}")
@ -566,7 +575,6 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
with self.event_lock:
if self.events["exit_early"]:
terminated_by_keyboard = True
# If we need to wait for human intervention, we note that outside the lock.
pause_policy = self.events["pause_policy"]
if pause_policy:
@ -580,6 +588,13 @@ class KeyboardInterfaceWrapper(gym.Wrapper):
# Execute the step in the underlying environment
obs, reward, terminated, truncated, info = self.env.step((policy_action, is_intervention))
# Override reward and termination if episode success event triggered
with self.event_lock:
if self.events["episode_success"]:
reward = 1
terminated_by_keyboard = True
return obs, reward, terminated or terminated_by_keyboard, truncated, info
def reset(self, **kwargs) -> Tuple[Any, Dict]: