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

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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 5d6879d93a
commit ee820859d3
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"