13 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…
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…
Robot Crash Course: Learning Soft and Stylized Falling
Pascal Strauch, David Müller, Sammy Christen +4
Despite recent advances in robust locomotion, bipedal robots operating in the real world remain at risk of falling. While most research focuses on preventing such events, we instea…
RobustDexGrasp: Robust Dexterous Grasping of General Objects
Hui Zhang, Zijian Wu, Linyi Huang +2
The ability to robustly grasp a variety of objects is essential for dexterous robots. In this paper, we present a framework for zero-shot dynamic dexterous grasping using single-vi…
Omnigrasp: Grasping Diverse Objects with Simulated Humanoids
Zhengyi Luo, Jinkun Cao, Sammy Christen +3
We present a method for controlling a simulated humanoid to grasp an object and move it to follow an object's trajectory. Due to the challenges in controlling a humanoid with dexte…
RILe: Reinforced Imitation Learning
Mert Albaba, Sammy Christen, Thomas Langarek +3
Acquiring complex behaviors is essential for artificially intelligent agents, yet learning these behaviors in high-dimensional settings poses a significant challenge due to the vas…