This repository provides a trained Action Chunking with Transformers (ACT) policy for the SO-101 robotic manipulation platform. The policy is trained entirely from human teleoperation demonstrations collected with the LeRobot framework. Given visual observations and robot proprioceptive states, the policy predicts a sequence of future robot actions and executes the learned manipulation behavior autonomously. The goal of this project is to learn a manipulation policy directly from human demonstrations, without explicitly programming the robot trajectory. The trained policy learns the following behavior: Detect → Approach → Grasp → Transport → Place Specifically, the robot is trained to: 1.…
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Hedgehog
hedgehog678
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