Learning Rotation-in-Place and Orbiting Policies for a Quadruped Robot

Reinforcement learning (RL) algorithms have successfully learned control policies for quadruped locomotion such as walking, rotation, and basic navigation. We utilize Proximal Policy Optimization and iGibson to train a quadruped robot in simulation to do two specific tasks—rotation-in-place and orbiting—with orbiting being a novel, previously unexplored task for quadruped robots. We show that with proper reward and environment engineering, we are able to train a simple two layer fully-connected neural network to do both tasks. We propose that both policies will be useful in a larger control system for a quadruped robot to explore its environment and that the orbiting policy is both novel and useful for learning more about certain objects of interest in the environment. See policy video here: https://youtu.be/olk2hJ372a4.

Date

2022-05

Resource Type

Text

Resource Subtype

Undergraduate Thesis

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