3 papers
cs.RO2026
Safe and Stable Neural Network Dynamical Systems for Robot Motion Planning
Allen Emmanuel Binny, Mahathi Anand, Hugo T. M. Kussaba +4
Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper,…
cs.RO2025
Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems
Shreenabh Agrawal, Hugo T. M. Kussaba, Lingyun Chen +4
Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One es…
cs.RO2025
Neural Control Barrier Functions from Physics Informed Neural Networks
Shreenabh Agrawal, Manan Tayal, Aditya Singh +1
As autonomous systems become increasingly prevalent in daily life, ensuring their safety is paramount. Control Barrier Functions (CBFs) have emerged as an effective tool for guaran…