分散Soft Actor-Criticによる物体条件付き探索の学習
Learning Object-conditioned Exploration using Distributed Soft Actor Critic
分散型Soft Actor-Criticを用いて、未知の実在住宅環境で指定物体を探索する物体誘導ナビゲーションポリシーを学習した研究。
著者: Ayzaan Wahid, Austin Stone, Kevin Chen, Brian Ichter, Alexander Toshev
分類: cs.RO
原文アブストラクト
Object navigation is defined as navigating to an object of a given label in a complex, unexplored environment. In its general form, this problem poses several challenges for Robotics: semantic exploration of unknown environments in search of an object and low-level control. In this work we study object-guided exploration and low-level control, and present an end-to-end trained navigation policy achieving a success rate of 0.68 and SPL of 0.58 on unseen, visually complex scans of real homes. We propose a highly scalable implementation of an off-policy Reinforcement Learning algorithm, distributed Soft Actor Critic, which allows the system to utilize 98M experience steps in 24 hours on 8 GPUs. Our system learns to control a differential drive mobile base in simulation from a stack of high dimensional observations commonly used on robotic platforms. The learned policy is capable of object-guided exploratory behaviors and low-level control learned from pure experiences in realistic environments.
