3 papers
cs.RO2026
BFMTrack: Latent Sequence Optimization for Physics-Based Motion Tracking with Behavioral Foundation Models
Thomas Rupf, Agon Serifi, David Müller +4
Behavioral Foundation Models (BFMs) offer a promising path toward universal physics-based character control by organizing a rich repertoire of physically plausible behaviors into a…
cs.RO2026
CoCo-InEKF: State Estimation with Learned Contact Covariances in Dynamic, Contact-Rich Scenarios
Michael Baumgartner, David Müller, Agon Serifi +4
Robust state estimation for highly dynamic motion of legged robots remains challenging, especially in dynamic, contact-rich scenarios. Traditional approaches often rely on binary c…
cs.RO2026
ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting
David Müller, Agon Serifi, Sammy Christen +3
Retargeting human kinematic reference motion onto a robot's morphology remains a formidable challenge. Existing methods often produce physical inconsistencies, such as foot sliding…