通过深度强化学习实现蛇形机器人在动态粘性环境中的自适应波动运动
Adaptive Undulatory Locomotion of Snake-like Robots in Dynamic Viscous Environments via Deep Reinforcement Learning
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中文总结 AI 辅助
研究蛇形机器人在动态粘性环境中的自适应运动，利用深度强化学习，通过非对称演员 - 评论家框架及特权信息提炼，使机器人获得非正弦自适应步态，提高推进速度和运输效率，突破传统控制极限。
中文摘要 AI 辅助
本文展示了深度强化学习（DRL）如何使蛇形机器人在动态变化的粘性环境中实现自适应运动，克服传统预定义控制方法的固有性能限制。由于缺乏用于流体特性的直接机载传感器，该任务被表述为部分可观测马尔可夫决策过程。通过采用非对称的演员 - 评论家框架，利用仅在物理模拟器中可用的特权信息训练的教师策略将其知识提炼为仅依赖本体感觉传感器信息的学生策略。在广泛的动态粘度变化（$10^{-7}$至$10^{-2} m^2/s$）范围内的模拟结果表明，DRL智能体自主获得非正弦自适应步态。这些步态提高了推进速度和运输效率，突破了传统正弦和运动控制的固有极限。研究结果表明，通过特权信息提炼进行隐式环境推理是在不可预测的流体动力学下绕过经典模型约束的有效方法。
英文摘要
This paper demonstrates how deep reinforcement learning (DRL) enables adaptive locomotion of snake-like robots in dynamically changing viscous environments, overcoming the inherent performance limitations of classical predefined control methods. The lack of direct onboard sensors for fluid properties necessitates formulating this task as a partially observable Markov decision process. By employing an asymmetric actor-critic framework, a teacher policy trained using privileged information available only in the physics simulator distills its knowledge into a student policy that relies solely on proprioceptive sensor information. Simulation results across a wide range of dynamic viscosity changes ($10^{-7}$ to $10^{-2} m^2/s$) reveal that the DRL agent autonomously acquires non-sinusoidal adaptive gaits. These gaits improve propulsion velocity and transport efficiency, breaking the inherent limits of conventional sinusoidal and kinematic control. The findings establish that implicit environment inference via privileged information distillation is an effective approach to bypass the constraints of classical models under unpredictable fluid dynamics.
