用动态规划生成步态，让机器人更快学会稳定走路
A Gait Driven Reinforcement Learning Framework for Humanoid Robots
- 将3D机器人拆成2D模型，用混合倒立摆模拟步态轨迹
- 设计三种奖励函数组合，显著缩短学习时间并提升行走性能
- 适用于需要快速适应新环境的仿人机器人研发
本文提出一种实时步态驱动的仿人机器人训练框架。首先，引入一种新型步态规划器，通过动力学建模设计期望关节轨迹；在步态设计过程中，将3D机器人模型解耦为两个2D模型，并近似为混合倒立摆（H-LIP）以进行轨迹规划，该规划器可在机器人学习环境中实时并行运行。其次，基于该步态规划器，在强化学习框架中设计三种有效奖励函数，构成奖励组合，实现周期性双足步态。该奖励组合显著减少机器人学习时间并提升运动性能。最后，通过仿真与实验对比，展示了所提方法的有效性。
原文摘要 · Abstract (English)
This paper presents a real-time gait driven training framework for humanoid robots. First, we introduce a novel gait planner that incorporates dynamics to design the desired joint trajectory. In the gait design process, the 3D robot model is decoupled into two 2D models, which are then approximated as hybrid inverted pendulums (H-LIP) for trajectory planning. The gait planner operates in parallel in real time within the robot's learning environment. Second, based on this gait planner, we design three effective reward functions within a reinforcement learning framework, forming a reward composition to achieve periodic bipedal gait. This reward composition reduces the robot's learning time and enhances locomotion performance. Finally, a gait design example, along with simulation and experimental comparisons, is presented to demonstrate the effectiveness of the proposed method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。