異なるダイナミクスを持つ実演者を模倣するための実行可能性学習
Learning Feasibility to Imitate Demonstrators with Different Dynamics
実演者と模倣者のダイナミクスが異なる場合でも、実演が模倣者にとって実行可能かを評価する指標を学習し、有益な実演を優先して模倣する手法を提案した。
著者: Zhangjie Cao, Yilun Hao, Mengxi Li, Dorsa Sadigh
分類: cs.RO, cs.LG
原文アブストラクト
The goal of learning from demonstrations is to learn a policy for an agent (imitator) by mimicking the behavior in the demonstrations. Prior works on learning from demonstrations assume that the demonstrations are collected by a demonstrator that has the same dynamics as the imitator. However, in many real-world applications, this assumption is limiting -- to improve the problem of lack of data in robotics, we would like to be able to leverage demonstrations collected from agents with different dynamics. This can be challenging as the demonstrations might not even be feasible for the imitator. Our insight is that we can learn a feasibility metric that captures the likelihood of a demonstration being feasible by the imitator. We develop a feasibility MDP (f-MDP) and derive the feasibility score by learning an optimal policy in the f-MDP. Our proposed feasibility measure encourages the imitator to learn from more informative demonstrations, and disregard the far from feasible demonstrations. Our experiments on four simulated environments and on a real robot show that the policy learned with our approach achieves a higher expected return than prior works. We show the videos of the real robot arm experiments on our website (https://sites.google.com/view/learning-feasibility).
