3 accepted papers
Soft robots present unique challenges for accurate modeling and control due to their virtually infinite degrees of freedom and highly nonlinear deformations. High-fidelity continuum models offer accuracy but are often computationally prohibitive for real-time control, while purely learning-based pol…
A key challenge towards reliable robotic control is devising computational models that can both learn policies and guarantee robustness when deployed in the field. Inspired by the free energy principle in computational neuroscience, to address these challenges, we propose a model for policy computat…
Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S²-NNDS, a learning-from-demonstration framework that simul…