Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion

Abstract

Algorithms for self-learning systems have made considerable progress in recent years, yet safety concerns and the need for additional instrumentation have so far largely limited learning experiments with real robots to well controlled lab settings. In this paper, we demonstrate how a small bipedal robot can autonomously learn to walk with minimal human intervention and with minimal instrumentation of the environment. We employ data-efficient off-policy deep reinforcement learning to learn to walk end-to-end, directly on hardware, using rewards that are computed exclusively from proprioceptive sensing. To allow the robot to autonomously adapt its behaviour to its environment, we additionally provide the agent with raw RGB camera images as input. By deploying two robots in different geographic locations while sharing data in a distributed learning setup, we achieve higher throughput and greater diversity of the training data. Our learning experiments constitute a step towards the long-term vision of learning "in the wild" for legged robots, and, to our knowledge, represent the first demonstration of learning a deep neural network controller for bipedal locomotion directly on hardware.