強化学習による実世界でのヒューマノイド歩行
Real-World Humanoid Locomotion with Reinforcement Learning
固有感覚と行動の履歴を入力とするTransformerベースの強化学習コントローラを訓練し、シミュレーションから実世界へゼロショットで転移して、屋外の多様な地形を歩行できるヒューマノイドロボットを実現した。
著者: Ilija Radosavovic, Tete Xiao, Bike Zhang, Trevor Darrell, Jitendra Malik, Koushil Sreenath
分類: cs.RO, cs.LG
原文アブストラクト
Humanoid robots that can autonomously operate in diverse environments have the potential to help address labour shortages in factories, assist elderly at homes, and colonize new planets. While classical controllers for humanoid robots have shown impressive results in a number of settings, they are challenging to generalize and adapt to new environments. Here, we present a fully learning-based approach for real-world humanoid locomotion. Our controller is a causal transformer that takes the history of proprioceptive observations and actions as input and predicts the next action. We hypothesize that the observation-action history contains useful information about the world that a powerful transformer model can use to adapt its behavior in-context, without updating its weights. We train our model with large-scale model-free reinforcement learning on an ensemble of randomized environments in simulation and deploy it to the real world zero-shot. Our controller can walk over various outdoor terrains, is robust to external disturbances, and can adapt in context.
