提出高效鲁棒的足式机器人轨迹优化框架，直接生成物理可行轨迹。
Rapid and Robust Trajectory Optimization for Humanoids
- 基于闭链运动学约束，端到端生成平滑轨迹
- 收敛速度比现有方法快，对初始猜测不敏感
- 开源实现，适用于高自由度人形机器人
针对高自由度人形机器人的轨迹设计计算复杂、超参数调优困难且依赖良好初始猜测的问题，本文提出一种通用步态优化框架，可直接生成平滑且符合物理规律的轨迹。该方法在收敛速度和鲁棒性上优于现有技术，并显式引入现代人形机器人常见的闭链运动学约束。算法已实现为开源C++代码库，发布于https://roahmlab.github.io/RAPTOR/。
原文摘要 · Abstract (English)
Performing trajectory design for humanoid robots with high degrees of freedom is computationally challenging. The trajectory design process also often involves carefully selecting various hyperparameters and requires a good initial guess which can further complicate the development process. This work introduces a generalized gait optimization framework that directly generates smooth and physically feasible trajectories. The proposed method demonstrates faster and more robust convergence than existing techniques and explicitly incorporates closed-loop kinematic constraints that appear in many modern humanoids. The method is implemented as an open-source C++ codebase which can be found at https://roahmlab.github.io/RAPTOR/.
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。