1 citations · 1 across the 5 of their papers we have counts for
7 papers · 1 filter
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…
HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincaré Maps, and Regions of Attraction
Blake Werner, Sergio A. Esteban, Massimiliano De Sa +2
Reduced-order models are powerful for analyzing and controlling high-dimensional dynamical systems. Yet constructing these models for complex hybrid systems such as legged robots r…
Architecture Is All You Need: Diversity-Enabled Sweet Spots for Robust Humanoid Locomotion
Blake Werner, Lizhi Yang, Aaron D. Ames
Robust humanoid locomotion in unstructured environments requires architectures that balance fast low-level stabilization with slower perceptual decision-making. We show that a simp…