基于深度强化学习的四足机器人鲁棒恢复控制器
Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning
AI总结：
本文提出基于无模型深度强化学习的分层行为控制器，通过四个神经网络策略协调控制四足机器人ANYmal的恢复动作，实现从任意跌倒姿态在5秒内恢复，测试成功率超97%。
摘要：
从跌倒中恢复的能力是腿足式机器人在复杂环境中鲁棒导航的关键特性。至今，该主题的研究进展甚微。现有解决方案大多基于（启发式）预定义轨迹，导致行为不自然，且需要大量工程努力来开发特定系统组件。本文提出一种基于无模型深度强化学习（RL）的方法，利用分层行为控制器控制四足机器人的恢复动作。该控制器由四个神经网络策略组成，包含三种行为及一个协调这些行为的行为选择器。每个策略均在仿真中独立训练，并直接部署于真实系统。我们在四足机器人ANYmal上实验验证了该方法，ANYmal是具有12个自由度的狗大小四足系统。采用我们的方法，ANYmal展现出动态且反应灵敏的恢复行为，能在不到5秒内从任意跌倒姿态中恢复。我们对该恢复动作测试了超过100次，成功率高于97%。
英文摘要：
The ability to recover from a fall is an essential feature for a legged robot to navigate in challenging environments robustly. Until today, there has been very little progress on this topic. Current solutions mostly build upon (heuristically) predefined trajectories, resulting in unnatural behaviors and requiring considerable effort in engineering system-specific components. In this paper, we present an approach based on model-free Deep Reinforcement Learning (RL) to control recovery maneuvers of quadrupedal robots using a hierarchical behavior-based controller. The controller consists of four neural network policies including three behaviors and one behavior selector to coordinate them. Each of them is trained individually in simulation and deployed directly on a real system. We experimentally validate our approach on the quadrupedal robot ANYmal, which is a dog-sized quadrupedal system with 12 degrees of freedom. With our method, ANYmal manifests dynamic and reactive recovery behaviors to recover from an arbitrary fall configuration within less than 5 seconds. We tested the recovery maneuver more than 100 times, and the success rate was higher than 97 %.
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