基于有限理性对抗强化学习的动态避障
Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning
AI总结：
针对四足机器人在未知动态障碍环境中的鲁棒导航问题，提出 Hi-QARL 分层对抗强化学习方法，通过量子响应均衡约束对手理性并设置理性课程，在随机迷宫和 Unitree GO1 仿真中验证了鲁棒性。
摘要：
强化学习（RL）已在为足式机器人获取稳定运动步态方面被证明大体有效。然而，设计能够在含障碍物的未知环境中鲁棒导航的控制算法，仍是四足运动领域一个持续存在的问题。为解决这一问题，一种便捷方式是通过分层方法来求解导航任务，即包含一个低层运动策略和一个高层导航策略。关键在于，高层策略需要对智能体路径上的动态障碍物具有鲁棒性。在本工作中，我们提出了一种赋予导航策略鲁棒性的新方法：在训练过程中遵循对抗式 RL 范式，将障碍物建模为对抗智能体。重要的是，为提高训练过程的可靠性，我们借助量子响应均衡来限制对抗智能体的理性，并针对其理性设置课程。我们将该方法命名为 Hierarchical policies via Quantal response Adversarial Reinforcement Learning（Hi-QARL，基于量子响应对抗强化学习的分层策略）。我们通过在含多个障碍物的未知随机迷宫中进行基准测试，证明了方法的鲁棒性。为证明其在真实场景中的适用性，我们在仿真中将该方法应用于 Unitree GO1 机器人。
英文摘要：
Reinforcement Learning (RL) has proven largely effective in obtaining stable locomotion gaits for legged robots. However, designing control algorithms which can robustly navigate unseen environments with obstacles remains an ongoing problem within quadruped locomotion. To tackle this, it is convenient to solve navigation tasks by means of a hierarchical approach with a low-level locomotion policy and a high-level navigation policy. Crucially, the high-level policy needs to be robust to dynamic obstacles along the path of the agent. In this work, we propose a novel way to endow navigation policies with robustness by a training process that models obstacles as adversarial agents, following the adversarial RL paradigm. Importantly, to improve the reliability of the training process, we bound the rationality of the adversarial agent resorting to quantal response equilibria, and place a curriculum over its rationality. We called this method Hierarchical policies via Quantal response Adversarial Reinforcement Learning (Hi-QARL). We demonstrate the robustness of our method by benchmarking it in unseen randomized mazes with multiple obstacles. To prove its applicability in real scenarios, our method is applied on a Unitree GO1 robot in simulation.
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