4 papers
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…
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…
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…
Kamino: GPU-based Massively Parallel Simulation of Multi-Body Systems with Challenging Topologies
Vassilios Tsounis, Guirec Maloisel, Christian Schumacher +6
We present Kamino, a GPU-based physics solver for massively parallel simulations of heterogeneous highly-coupled mechanical systems. Implemented in Python using NVIDIA Warp and int…