4 papers
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,…
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…
Compositional Construction of Barrier Functions for Switched Impulsive Systems
Katharina Bieker, Hugo Tadashi Kussaba, Philipp Scholl +4
Many systems occurring in real-world applications, such as controlling the motions of robots or modeling the spread of diseases, are switched impulsive systems. To ensure that the…
Optimal Control for Clutched-Elastic Robots: A Contact-Implicit Approach
Dennis Ossadnik, Vasilije RakÄeviÄ, Mehmet C. Yildirim +4
Intrinsically elastic robots surpass their rigid counterparts in a range of different characteristics. By temporarily storing potential energy and subsequently converting it to kin…