将基于模型的脚步规划与无模型强化学习相结合以实现动态腿足运动
Integrating Model-Based Footstep Planning with Model-Free Reinforcement Learning for Dynamic Legged Locomotion
AI总结：
本文提出一种结合LIP模型脚步规划与强化学习的控制框架，仅以期望落脚位置作为引导训练策略，在MIT Humanoid上实现了稳定动态行走、转弯及崎岖地形适应，硬件实测前向速度达1.5 m/s。
摘要：
在这项工作中，我们引入了一个控制框架，该框架将基于模型的脚步规划与强化学习（RL）相结合，利用从线性倒立摆（LIP）动力学推导出的期望脚步模式。利用LIP模型，我们的方法前向预测机器人状态，并在给定速度指令的情况下确定期望的落脚位置。然后，我们训练一个RL策略来跟踪这些落脚位置，而无需跟随从LIP模型推导出的完整参考运动。这种来自物理模型的部分引导使RL策略能够整合物理信息动力学的预测能力与RL控制器的适应性特征，同时避免策略过度拟合模板模型。我们的方法在MIT Humanoid上得到了验证，证明我们的策略能够实现稳定且动态的行走和转弯运动。我们进一步通过将运动任务扩展到未见过的崎岖地形，验证了策略的适应性和泛化能力。在硬件部署过程中，我们在跑步机上实现了高达1.5 m/s的前向行走速度，并成功执行了90度和180度转弯等动态运动操作。
英文摘要：
In this work, we introduce a control framework that combines model-based footstep planning with Reinforcement Learning (RL), leveraging desired footstep patterns derived from the Linear Inverted Pendulum (LIP) dynamics. Utilizing the LIP model, our method forward predicts robot states and determines the desired foot placement given the velocity commands. We then train an RL policy to track the foot placements without following the full reference motions derived from the LIP model. This partial guidance from the physics model allows the RL policy to integrate the predictive capabilities of the physics-informed dynamics and the adaptability characteristics of the RL controller without overfitting the policy to the template model. Our approach is validated on the MIT Humanoid, demonstrating that our policy can achieve stable yet dynamic locomotion for walking and turning. We further validate the adaptability and generalizability of our policy by extending the locomotion task to unseen, uneven terrain. During the hardware deployment, we have achieved forward walking speeds of up to 1.5 m/s on a treadmill and have successfully performed dynamic locomotion maneuvers such as 90-degree and 180-degree turns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
