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base_model: |
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- openvla/openvla-7b |
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- rail-berkeley/octo-base |
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This repository contains [OpenVLA](https://huggingface.co/openvla/openvla-7b) and [Octo](https://huggingface.co/rail-berkeley/octo-base) checkpoints fine-tuned on tele-op and RL generated datasets for the Connctor Insertion task in the paper [RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning |
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](https://generalist-distillation.github.io/). |
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Each model is trained on 45 episodes either collected by human tele-operation or rolling out converged RL policies. Each dataset contains 15 episodes for a VGA connector, 15 episodes for a USB-A connector, and 15 episodes for an ethernet connector. The models take 1 wrist camera and 1 language command as input, and outputs a 6D end-effector twist expressed in the wrist frame. |
