Autonomous robots would benefit a lot by gaining the ability to manipulate their environment to solve path planning tasks, known as the Navigation Among Movable Obstacle (NAMO) problem. In this paper, we present a deep reinforcement learning approach for solving NAMO locally, near narrow passages. We train parallel agents in physics simulation using an Advantage Actor-Critic based algorithm with a multi-modal neural network. We present an online policy that is able to push obstacles in a non-axial-aligned fashion, react to unexpected obstacle dynamics in real-time, and solve the local NAMO problem. Experimental validation in simulation shows that the presented approach generalises to unseen NAMO problems in unknown environments. We further demonstrate the implementation of the policy on a real quadrupedal robot, showing that the policy can deal with real-world sensor noises and uncertainties in unseen NAMO tasks.

翻译：自主机器人若能具备操纵环境以解决路径规划任务的能力，将从中获益良多，这类任务被称为可移动障碍物导航（NAMO）问题。本文提出了一种基于深度强化学习的局部导航方法，专门用于解决窄通道附近的NAMO问题。我们利用基于优势演员-评论家算法的多模态神经网络，在物理仿真环境中训练并行智能体。所提出的在线策略能够以非轴向对齐方式推动障碍物，实时响应意外的障碍物动力学变化，并解决局部NAMO问题。仿真实验验证表明，该方法能够泛化至未知环境中的未见过NAMO问题。我们进一步在真实四足机器人上实现了该策略，证明其能够处理真实世界中的传感器噪声和不确定性，完成未见过的NAMO任务。