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20242026
most citedASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills

2 citations · 5 across the 7 of their papers we have counts for

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cs.RO2026

Hybrid Feedback Sampling for Sample-Efficient Model Predictive Control

Chaoyi Pan, Zeji Yi, John Zhang +3

Thanks to its parallelizability and flexibility, sampling-based Model Predictive Control (MPC) has become widely popular for controlling real-world robotic systems. However, for hi…

cs.RO2025

Whole-Body Model-Predictive Control of Legged Robots with MuJoCo

John Z. Zhang, Taylor A. Howell, Zeji Yi +6

We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model-predictive control (MPC) of quadruped and humanoid robots: the iterative LQR (i…

cs.RO2025★ 2 cited

ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills

Tairan He, Jiawei Gao, Wenli Xiao +15

Humanoid robots hold the potential for unparalleled versatility in performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a s…

cs.RO2024★ 1 cited

Full-Order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing

Haoru Xue, Chaoyi Pan, Zeji Yi +2

Due to high dimensionality and non-convexity, real-time optimal control using full-order dynamics models for legged robots is challenging. Therefore, Nonlinear Model Predictive Con…

cs.RO2024★ 1 cited

Model-Based Diffusion for Trajectory Optimization

Chaoyi Pan, Zeji Yi, Guanya Shi +1

Recent advances in diffusion models have demonstrated their strong capabilities in generating high-fidelity samples from complex distributions through an iterative refinement proce…