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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>
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Remi
2024-05-31 15:31:02 +02:00
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parent 504d2aaf48
commit d585c73f9f
22 changed files with 525 additions and 140 deletions
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lerobot/common/policies/diffusion/configuration_diffusion.py
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@@ -26,21 +26,26 @@ class DiffusionConfig:
The parameters you will most likely need to change are the ones which depend on the environment / sensors.
Those are: `input_shapes` and `output_shapes`.
Notes on the inputs and outputs:
- "observation.state" is required as an input key.
- A key starting with "observation.image is required as an input.
- "action" is required as an output key.
Args:
n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the
current step and additional steps going back).
horizon: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`.
n_action_steps: The number of action steps to run in the environment for one invocation of the policy.
See `DiffusionPolicy.select_action` for more details.
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, shapes doesnt 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, shapes doesnt include batch dimension or temporal dimension.
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