From the 1 of 23 linked papers with an AI index.
1 citations · 1 across the 7 of their papers we have counts for
23 papers
Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting
Sergio A. Esteban, Jason H. K. Siu, Derrick Mach +4
Motion retargeting approaches often prioritize kinematic similarity over whole-body dynamics, contact consistency, and actuation limits, yielding references that are difficult for…
PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball
Lizhi Yang, Junheng Li, Aaron D. Ames
The paper introduces PAC-MAN, a perception-aware control-barrier-function reinforcement learning framework that enables a humanoid robot to safely dodge balls using depth segmentat…
TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutes
Blake Werner, Ilona Demler, Pietro Perona +1
How do we learn to hit a tennis backhand? Not from a thousand hours of tennis tournaments on TV - we work with a coach and practice. We argue this is also the right recipe for teac…
CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions
Lizhi Yang, Blake Werner, Massimiliano de Sa +1
Reinforcement learning (RL), while powerful and expressive, can often prioritize performance at the expense of safety. Yet safety violations can lead to catastrophic outcomes in re…
Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models
Lizhi Yang, Blake Werner, Adrian B. Ghansah +1
Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unified framework for walking control…
SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics
Lizhi Yang, Blake Werner, Ryan K. Cosner +3
Robot learning has produced remarkably effective ``black-box'' controllers for complex tasks such as dynamic locomotion on humanoids. Yet ensuring dynamic safety, i.e., constraint…