クロスモーダルTransformerによる視覚誘導型四足歩行のエンドツーエンド学習
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers
固有感覚と深度画像をTransformerで統合し、障害物や不整地を先読みして歩行する四足ロボットの強化学習手法を提案。シミュレーションから実機へ転移し、未知環境での汎化性能を大幅に向上させた。
著者: Ruihan Yang, Minghao Zhang, Nicklas Hansen, Huazhe Xu, Xiaolong Wang
分類: cs.LG, cs.CV, cs.RO
原文アブストラクト
We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL) with a Transformer-based model that learns to combine proprioceptive information and high-dimensional depth sensor inputs. While learning-based locomotion has made great advances using RL, most methods still rely on domain randomization for training blind agents that generalize to challenging terrains. Our key insight is that proprioceptive states only offer contact measurements for immediate reaction, whereas an agent equipped with visual sensory observations can learn to proactively maneuver environments with obstacles and uneven terrain by anticipating changes in the environment many steps ahead. In this paper, we introduce LocoTransformer, an end-to-end RL method that leverages both proprioceptive states and visual observations for locomotion control. We evaluate our method in challenging simulated environments with different obstacles and uneven terrain. We transfer our learned policy from simulation to a real robot by running it indoors and in the wild with unseen obstacles and terrain. Our method not only significantly improves over baselines, but also achieves far better generalization performance, especially when transferred to the real robot. Our project page with videos is at https://rchalyang.github.io/LocoTransformer/ .
