1 citations · 1 across the 12 of their papers we have counts for
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PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball
Lizhi Yang, Junheng Li, Aaron D. Ames
We present PAC-MAN, a perception-aware CBF-RL framework that couples control-barrier safety with deployment-realistic onboard sensing for whole-body humanoid dodgeball. The deploye…
HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
Lizhi Yang, Junheng Li, Nehar Poddar +5
Humanoid loco-manipulation benefits from one controller to coordinate locomotion, arm movements, and fall recovery. These behaviors are difficult to learn together from scratch bec…
Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation
Junheng Li, Liang Wu, Sergio A. Esteban +3
In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (RL) enables robust whole-bod…
MIRROR: Visual Motion Imitation via Real-time Retargeting and Teleoperation with Parallel Differential Inverse Kinematics
Junheng Li, Lizhi Yang, Aaron D. Ames
Real-time humanoid teleoperation requires inverse kinematics (IK) solvers that are both responsive and constraint-safe under kinematic redundancy and self-collision constraints. Wh…
Safe-SAGE: Social-Semantic Adaptive Guidance for Safe Engagement through Laplace-Modulated Poisson Safety Functions
Lizhi Yang, Ryan M. Bena, Meg Wilkinson +4
Traditional safety-critical control methods, such as control barrier functions, suffer from semantic blindness, exhibiting the same behavior around obstacles regardless of contextu…
Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds
Min Dai, William D. Compton, Junheng Li +2
Bipedal humanoid robots must precisely coordinate balance, timing, and contact decisions when locomoting on constrained footholds such as stepping stones, beams, and planks -- even…
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…
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…
Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Ryan M. Bena, Gilbert Bahati, Blake Werner +3
Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular,…
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…
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning
Chong Zhang, Lizhi Yang
Terrain-aware locomotion has become an emerging topic in legged robotics. However, it is hard to generate diverse, challenging, and realistic unstructured terrains in simulation, w…