3 papers
cs.RO2026
Learning from Demonstrations over Riemannian Manifolds using Neural ODEs: An Extended Abstract
Diana Cuervo Espinosa, Mahathi Anand, Angela P. Schoellig
Learning from demonstratins (LfD) is usually performed over Euclidean spaces, while the robot state, e.g. orientation, naturally evolves over curved spaces. Therefore, to ensure na…
cs.RO2026
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…
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,…