Learning to Walk in the Real World with Minimal Human Effort
This work addresses the problem of reliable and stable locomotion for legged robots, which is a fundamental challenge in robotics, by reducing human effort in training.
The paper tackled the challenge of enabling legged robots to learn locomotion autonomously in real-world settings with minimal human intervention, achieving efficient learning on varied terrains like flat ground, a soft mattress, and a doormat with crevices using a Minitaur robot.
Reliable and stable locomotion has been one of the most fundamental challenges for legged robots. Deep reinforcement learning (deep RL) has emerged as a promising method for developing such control policies autonomously. In this paper, we develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort. The key difficulties for on-robot learning systems are automatic data collection and safety. We overcome these two challenges by developing a multi-task learning procedure and a safety-constrained RL framework. We tested our system on the task of learning to walk on three different terrains: flat ground, a soft mattress, and a doormat with crevices. Our system can automatically and efficiently learn locomotion skills on a Minitaur robot with little human intervention. The supplemental video can be found at: \url{https://youtu.be/cwyiq6dCgOc}.