most citedLearning Human-to-Humanoid Real-Time Whole-Body Teleoperation

3 citations · 8 across the 8 of their papers we have counts for

collaborators

9 papers

cs.RO2026

Humanoid Safe Stop via Learned Stoppability Value

Junfeng Long, Pieter Abbeel, Koushil Sreenath +3

Humanoid robots responding to emergency stop commands typically execute a fixed maneuver, without reasoning about whether a safe stop is actually feasible from the current state. W…

cs.RO20241 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…

cs.RO2024

OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning

Tairan He, Zhengyi Luo, Xialin He +6

We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, Omni…

cs.MA20241 cited

Adaptive Decision-Making for Autonomous Vehicles: A Learning-Enhanced Game-Theoretic Approach in Interactive Environments

Heye Huang, Jinxin Liu, Guanya Shi +3

This paper proposes an adaptive behavioral decision-making method for autonomous vehicles (AVs) focusing on complex merging scenarios. Leveraging principles from non-cooperative ga…

cs.RO20243 cited

Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation

Tairan He, Zhengyi Luo, Wenli Xiao +4

We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB ca…

cs.LG20241 cited

CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design

Zeji Yi, Chaoyi Pan, Guanqi He +2

Sampling-based Model Predictive Control (MPC) has been a practical and effective approach in many domains, notably model-based reinforcement learning, thanks to its flexibility and…