Instructions to use jaheroth/act_pusht_dec7_bs8_800k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use jaheroth/act_pusht_dec7_bs8_800k with LeRobot:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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Download README.md from jaheroth/act_pusht_dec7_bs8_800k: direct link, hf CLI and curl.
- Browser
- Download file 465 Bytes
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https://huggingface.co/jaheroth/act_pusht_dec7_bs8_800k/resolve/main/README.md
- Command line
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hf download hf://jaheroth/act_pusht_dec7_bs8_800k/README.md
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curl -L -o README.md https://huggingface.co/jaheroth/act_pusht_dec7_bs8_800k/resolve/main/README.md
465 Bytes
| license: apache-2.0 | |
| tags: [robotics, act, pusht, lerobot] | |
| # act_pusht_dec7_bs8_800k | |
| Batch-8 equal-sample-budget run (800k steps = 6.4M samples): 159.1 avg_sum_imputed_reward, 60.2% success (n=5000). Matches batch-64 at 100k steps to one decimal. | |
| Part of a 6-week robot-learning training block (https://github.com/JaHeRoth/robot-learning). Evaluated on gym-pusht at | |
| n_action_steps=16; avg_sum_imputed_reward imputes 0.95/step to horizon 300 after success. | |