Can I read AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale on EtoBox?

AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale by Lu, Yao; Hausman, Karol; Chebotar, Yevgen; Yan, Mengyuan; Jang, Eric; Herzog, Alexander; Xiao, Ted; Irpan, Alex; Khansari, Mohi; Kalashnikov, Dmitry; Levine, Sergey is a scholarly article available to read on EtoBox.

What is AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale about?

Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof autonomously collected experience.Both methods have complementarystrengths and weaknesses: RL can reach a high level of performance, but requiresexploration, which can be very time consuming and unsafe; IL does not requireexploration, but only learns skills that are as good as the provided demonstrations.Can a single method combine the strengths of both approaches? A number ofprior methods have aimed to address this question, proposing a variety of tech-niques that integrate elements of IL and RL. However, scaling up such methodsto complex robotic skills that integrate diverse offline data and generalize mean-ingfully to real-world scenarios still presents a major challenge. In this paper, ouraim is to test the scalability of prior IL + RL algorithms and devise a system basedon detailed empirical experimentation that combines existing components in themost effective and scalable way. To that end, we present a series of experimentsaimed at understanding the implications of each design decision, so as to develop acombined approach that

Author

Lu, Yao; Hausman, Karol; Chebotar, Yevgen; Yan, Mengyuan; Jang, Eric; Herzog, Alexander; Xiao, Ted; Irpan, Alex; Khansari, Mohi; Kalashnikov, Dmitry; Levine, Sergey

Published

2021

Language

EN