Instructions to use Jingyi-Z/act-sotac-tac-pred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use Jingyi-Z/act-sotac-tac-pred 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
act-sotac-tac-pred
ACT policy with one added transformer encoder token from a tactile encoder. Variant: Encoder over a 10 frame tactile window, initialized from self supervised pretraining (predict the current frame with it masked), then finetuned.
Trained on episodes 0 to 70 of Jingyi-Z/sotac (red foam ball into container, SO-101, top and wrist cameras, Paxini fingertip forces at 30 Hz), 100k steps, batch 8, stock ACT hyperparameters, lerobot 0.4.4 with a tactile-encoder patch. The config carries tactile_key, tactile_hidden and related fields: apply the matching ACT patch before loading, or strip those keys for stock lerobot. Companion variants: act-sotac-tac-{mlp,pred,esn,esnpred}. Baseline without tactile: act-sotac-runA.
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