Instructions to use Menlo/asimov1-locomotion-0818 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Asimov
How to use Menlo/asimov1-locomotion-0818 with Asimov:
# Run the policy in a browser simulation of the Asimov 1 humanoid (no GPU needed) git clone https://github.com/menloresearch/humanoid-policy-viewer cd humanoid-policy-viewer npm install npm run hf Menlo/asimov1-locomotion-0818
- Notebooks
- Google Colab
- Kaggle
Metadata for robotics policy model for Asimov 🔥 (#1)
Browse files- Metadata for robotics policy model for Asimov 🔥 (666fa2785f21ea73fca0a93c4c3d9f353be983de)
Co-authored-by: Julien Chaumond <julien-c@users.noreply.huggingface.co>
README.md
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license: bsd-3-clause
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- `policy.onnx` is the exported policy for inference, producing joint-position actions.
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- `agent.yaml` records the actor-critic and AMP training settings, including the motion data configuration.
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- `env.yaml` records the simulation and locomotion task settings, including observations, actions, commands, and rewards.
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- Training and evaluation code: [menloresearch/isaac_asimov](https://github.com/menloresearch/isaac_asimov).
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library_name: asimov
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pipeline_tag: robotics
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license: bsd-3-clause
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- `policy.onnx` is the exported policy for inference, producing joint-position actions.
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- `agent.yaml` records the actor-critic and AMP training settings, including the motion data configuration.
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- `env.yaml` records the simulation and locomotion task settings, including observations, actions, commands, and rewards.
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- Training and evaluation code: [menloresearch/isaac_asimov](https://github.com/menloresearch/isaac_asimov).
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