用神经网络同时学运动、安全和稳定性，让机器人更安全地从演示中学习复杂动作。
Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning
- 用神经网络建模动力系统，联合学习运动轨迹与安全稳定性约束。
- 在2D/3D场景中成功从不安全示范中学习出鲁棒的运动轨迹。
- 支持概率性安全保证，适合高可靠性机器人控制任务。
从示范中学习安全且稳定的机器人运动仍具挑战，尤其在涉及动态障碍物的复杂非线性任务中。本文提出S²-NNDS框架，通过神经网络联合学习表达性强的动力系统，以及神经李雅普诺夫稳定性与屏障安全证书。相比传统依赖多项式参数化的方案，S²-NNDS能捕捉复杂运动模式，并利用分裂共形预测在学习到的证书上提供概率性保障。在多种2D与3D数据集（包括LASA手写数据及从Franka Emika Panda机械臂采集的本体感觉示范）上的实验验证了该方法从潜在不安全示范中学习鲁棒、安全、稳定运动的有效性。源代码、补充材料及实验视频可访问 https://github.com/allemmbinn/S2NNDS。
原文摘要 · Abstract (English)
Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S$^2$-NNDS, a learning-from-demonstration framework that simultaneously learns expressive neural dynamical systems alongside neural Lyapunov stability and barrier safety certificates. Unlike traditional approaches with restrictive polynomial parameterizations, S$^2$-NNDS leverages neural networks to capture complex robot motions, providing probabilistic guarantees through split conformal prediction in learned certificates. Experimental results in various 2D and 3D datasets -- including LASA handwriting and demonstrations recorded kinesthetically from the Franka Emika Panda robot -- validate the effectiveness of S$^2$-NNDS in learning robust, safe, and stable motions from potentially unsafe demonstrations. The source code, supplementary material and experiment videos can be accessed via https://github.com/allemmbinn/S2NNDS
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。