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autonomy

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Robotics-Object-Pose-Estimation

A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

  • Updated Apr 13, 2022
  • Python

This package provides a CLF-based reactive planning system, described in paper: Efficient Anytime CLF Reactive Planning System for a Bipedal Robot on Undulating Terrain. The reactive planning system consists of a 5-Hz planning thread to guide a robot to a distant goal and a 300-Hz Control-Lyapunov-Function-based (CLF-based) reactive thread to cope with robot deviations. The planning system allowed Cassie Blue to autonomously traverse sinusoidally varying terrain. More experiments are still being conducted and this repo and the paper will be updated accordingly.

  • Updated Sep 7, 2021
  • C++

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