学習した潜在ダイナミクスを用いたヒューマノイドのマップレスナビゲーション
Mapless Humanoid Navigation Using Learned Latent Dynamics
学習した潜在空間で開ループ軌道を生成し、視覚と自己状態からヒューマノイドのマップレスナビゲーションを実現する深層強化学習手法を提案し、実機で検証した。
著者: Andre Brandenburger, Diego Rodriguez, Sven Behnke
分類: cs.RO
原文アブストラクト
In this paper, we propose a novel Deep Reinforcement Learning approach to address the mapless navigation problem, in which the locomotion actions of a humanoid robot are taken online based on the knowledge encoded in learned models. Planning happens by generating open-loop trajectories in a learned latent space that captures the dynamics of the environment. Our planner considers visual (RGB images) and non-visual observations (e.g., attitude estimations). This confers the agent upon awareness not only of the scenario, but also of its own state. In addition, we incorporate a termination likelihood predictor model as an auxiliary loss function of the control policy, which enables the agent to anticipate terminal states of success and failure. In this manner, the sample efficiency of the approach for episodic tasks is increased. Our model is evaluated on the NimbRo-OP2X humanoid robot that navigates in scenes avoiding collisions efficiently in simulation and with the real hardware.
