- Added lerobot/scripts/server/gym_manipulator.py that contains all the necessary wrappers to run a gym-style env around the real robot.
- Added `lerobot/scripts/server/find_joint_limits.py` to test the min and max angles of the motion you wish the robot to explore during RL training. - Added logic in `manipulator.py` to limit the maximum possible joint angles to allow motion within a predefined joint position range. The limits are specified in the yaml config for each robot. Checkout the so100.yaml. Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
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AdilZouitine
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@@ -4,7 +4,7 @@ defaults:
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- _self_
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seed: 13
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dataset_repo_id: aractingi/pick_place_lego_cube_1
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dataset_repo_id: aractingi/push_green_cube_hf_cropped_resized
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train_split_proportion: 0.8
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# Required by logger
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@@ -24,7 +24,8 @@ training:
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eval_freq: 1 # How often to run validation (in epochs)
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save_freq: 1 # How often to save checkpoints (in epochs)
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save_checkpoint: true
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image_keys: ["observation.images.top", "observation.images.wrist"]
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# image_keys: ["observation.images.top", "observation.images.wrist"]
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image_keys: ["observation.images.laptop", "observation.images.phone"]
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label_key: "next.reward"
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eval:
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@@ -32,7 +33,7 @@ eval:
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num_samples_to_log: 30 # Number of validation samples to log in the table
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policy:
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name: "hilserl/classifier/pick_place_lego_cube_1"
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name: "hilserl/classifier/push_green_cube_hf_cropped_resized" #"hilserl/classifier/pick_place_lego_cube_1"
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model_name: "facebook/convnext-base-224"
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model_type: "cnn"
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num_cameras: 2 # Has to be len(training.image_keys)
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@@ -14,6 +14,9 @@ calibration_dir: .cache/calibration/so100
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# Set this to a positive scalar to have the same value for all motors, or a list that is the same length as
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# the number of motors in your follower arms.
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max_relative_target: null
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joint_position_relative_bounds:
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min: [-88.50586, 23.81836, 0.87890625, -32.16797, 78.66211, 0.53691274]
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max: [84.55078, 187.11914, 145.98633, 101.60156, 146.60156, 88.18792]
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leader_arms:
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main:
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