IEEE RA-L

Discovering Optimal Natural Gaits of Dissipative Systems via Virtual Energy Injection

通过虚拟能量注入发现耗散系统的最优自然步态

Korbinian Griesbauer, Davide Calzolari, Maximilian Raff, C. David Remy, Alin Albu-Schäffer

AI总结 本文通过虚拟能量注入技术，发现耗散系统中利用自然动力学的最优自然步态，提升腿部机器人的能量效率和适应性。

Comments Preprint Version, IEEE Robotics and Automation Letters (RA-L), accepted November 2025

An Alignment-Based Approach to Learning Motions from Demonstrations

Alex Cuellar, Christopher K Fourie, Julie A Shah

机构 * Massachusetts Institute of Technology（麻省理工学院） 纠错

Comments 8 pages, 8 figures, originally published in the IEEE Robotics and Automation Letters

Journal ref IEEE Robotics and Automation Letters, vol. 10, no. 11, pp. 11912-11919, Nov. 2025