1 citations · 1 across the 24 of their papers we have counts for
28 papers
HULK: Learning Whole-Body Forceful Loco-Manipulation for Humanoids
An Dang, Arturo Flores Alvarez, Yu-Ming Chen +5
Humanoid loco-manipulation of large, heavy objects demands forceful interaction across the entire body. However, such payloads shift a humanoid's center of mass and impose sustaine…
RACER: Residual-Adaptive Closed-Loop Estimation for Sampling-Based Planning in Wheeled-Quadruped Racing
Yuxiang Liu, Marla Eisman, Lizhi Yang +2
We present RACER, a hierarchical control framework for wheel-based quadruped racing that combines an MPPI planner with a learned residual dynamics model and a low-level RL velocity…
The Layer Mystery of VLA: An Information-Theoretical Analysis of VLA Latent Interface
Yuxiang Liu, Lizhi Yang, Fengze Xie +2
Vision-language-action (VLA) policies connect a pretrained vision-language backbone to an action head through a latent interface, but which backbone layers this interface should ex…
LIMBO: Learning and Internalizing Model-Free Barrier Objectives for Agile and Safe Whole-Body Control
Jake Gonzales, Arturo Flores Alvarez, Yu-Ming Chen +3
Safe whole-body control requires coordinating collision avoidance and balance under high-dimensional, nonlinear dynamics--making safety certificates difficult to design and reuse a…
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
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…
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…
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…
On Surprising Effects of Risk-Aware Domain Randomization for Contact-Rich Sampling-based Predictive Control
Sergio A. Esteban, Junheng Li, Vince Kurtz +1
Domain randomization (DR) is widely used in policy learning to improve robustness to modeling error, but remains underexplored in contact-rich sampling-based predictive control (SP…
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…
Integral Control Barrier Functions with Input Delay: Prediction, Feasibility, and Robustness
Adam K. Kiss, Ersin Das, Tamas G. Molnar +1
Time delays in feedback control loops can cause controllers to respond too late, and with excessively large corrective actions, leading to unsafe behavior (violation of state const…
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…
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…
MPC: A Parallel-in-horizon and Construction-free NMPC Solver
Liang Wu, Bo Yang, Junheng Li +5
The alternating direction method of multipliers (ADMM) has gained increasing popularity in embedded model predictive control (MPC) due to its code simplicity and pain-free paramete…
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…
Probabilistic Control Barrier Functions: Safety in Probability for Discrete-Time Stochastic Systems
Pol Mestres, Blake Werner, Ryan K. Cosner +1
Control systems operating in the real world face countless sources of unpredictable uncertainties. These random disturbances can render deterministic guarantees inapplicable and ca…
Chasing Stability: Humanoid Running via Control Lyapunov Function Guided Reinforcement Learning
Zachary Olkin, Kejun Li, William D. Compton +1
Achieving highly dynamic behaviors on humanoid robots, such as running, requires controllers that are both robust and precise, and hence difficult to design. Classical control meth…
RoMoCo: Robotic Motion Control Toolbox for Reduced-Order Model-Based Locomotion on Bipedal and Humanoid Robots
Min Dai, Aaron D. Ames
We present RoMoCo, an open-source C++ toolbox for the synthesis and evaluation of reduced-order model-based planners and whole-body controllers for bipedal and humanoid robots. RoM…
Hierarchical Reduced-Order Model Predictive Control for Robust Locomotion on Humanoid Robots
Adrian B. Ghansah, Sergio A. Esteban, Aaron D. Ames
As humanoid robots enter real-world environments, ensuring robust locomotion across diverse environments is crucial. This paper presents a computationally efficient hierarchical co…
A Layered Control Perspective on Legged Locomotion: Embedding Reduced Order Models via Hybrid Zero Dynamics
Sergio A. Esteban, Max H. Cohen, Adrian B. Ghansah +1
Reduced-order models (ROMs) provide a powerful means of synthesizing dynamic walking gaits on legged robots. Yet this approach lacks the formal guarantees enjoyed by methods that u…
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,…
Locomotion on Constrained Footholds via Layered Architectures and Model Predictive Control
Zachary Olkin, Aaron D. Ames
Computing stabilizing and optimal control actions for legged locomotion in real time is difficult due to the nonlinear, hybrid, and high dimensional nature of these robots. The hyb…