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Add real-world support for ACT on Aloha/Aloha2 (#228)

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Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>
This commit is contained in:
Remi
2024-05-31 15:31:02 +02:00
committed by GitHub
parent 504d2aaf48
commit d585c73f9f
22 changed files with 525 additions and 140 deletions
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9
lerobot/common/policies/tdmpc/configuration_tdmpc.py
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@@ -31,6 +31,15 @@ class TDMPCConfig:
n_action_repeats: The number of times to repeat the action returned by the planning. (hint: Google
action repeats in Q-learning or ask your favorite chatbot)
horizon: Horizon for model predictive control.
input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
the input data name, and the value is a list indicating the dimensions of the corresponding data.
For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96],
indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't
include batch dimension or temporal dimension.
output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents
the output data name, and the value is a list indicating the dimensions of the corresponding data.
For example, "action" refers to an output shape of [14], indicating 14-dimensional actions.
Importantly, `output_shapes` doesn't include batch dimension or temporal dimension.
input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
and the value specifies the normalization mode to apply. The two available modes are "mean_std"
which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a