7 citations · 26 across the 23 of their papers we have counts for
6 papers · 1 filter
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,…
A Unified Multi-Dynamics Framework for Perception-Oriented Modeling in Tendon-Driven Continuum Robots
Ibrahim Alsarraj, Yuhao Wang, Abdalla Swikir +4
Tendon-driven continuum robots offer intrinsically safe and contact-rich interactions owing to their kinematic redundancy and structural compliance. However, their perception often…
Stable Robot Motions on Manifolds: Learning Lyapunov-Constrained Neural Manifold ODEs
David Boetius, Abdelrahman Abdelnaby, Ashok Kumar +3
Learning stable dynamical systems from data is crucial for safe and reliable robot motion planning and control. However, extending stability guarantees to trajectories defined on R…
Density Matrix-based Dynamics for Quantum Robotic Swarms
Maria Mannone, Mahathi Anand, Peppino Fazio +1
In a robotic swarm, parameters such as position and proximity to the target can be described in terms of probability amplitudes. This idea led to recent studies on a quantum approa…
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…
Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
Lingyun Chen, Xinrui Zhao, Marcos P. S. Campanha +4
This paper presents a novel approach for robot navigation in environments containing deformable obstacles. By integrating Learning from Demonstration (LfD) with Dynamical Systems (…