3 citations · 3 across the 2 of their papers we have counts for
5 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…
Optimal Control for Clutched-Elastic Robots: A Contact-Implicit Approach
Dennis Ossadnik, Vasilije Rakčević, 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…
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…
Learning Barrier-Certified Polynomial Dynamical Systems for Obstacle Avoidance with Robots
Martin Schonger, Hugo T. M. Kussaba, Lingyun Chen +4
Established techniques that enable robots to learn from demonstrations are based on learning a stable dynamical system (DS). To increase the robots' resilience to perturbations dur…