4 citations · 12 across the 8 of their papers we have counts for
8 papers
HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation
Zhecheng Yuan, Tianming Wei, Langzhe Gu +4
Leveraging human motion data to impart robots with versatile manipulation skills has emerged as a promising paradigm in robotic manipulation. Nevertheless, translating multi-source…
HDP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning
Yiyang Lu, Yufeng Tian, Zhecheng Yuan +4
Visuomotor policy learning has witnessed substantial progress in robotic manipulation, with recent approaches predominantly relying on generative models to model the action distrib…
Stem-OB: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversion
Kaizhe Hu, Zihang Rui, Yao He +3
Visual imitation learning methods demonstrate strong performance, yet they lack generalization when faced with visual input perturbations, including variations in lighting and text…
On the Evaluation of Generative Robotic Simulations
Feng Chen, Botian Xu, Pu Hua +4
Due to the difficulty of acquiring extensive real-world data, robot simulation has become crucial for parallel training and sim-to-real transfer, highlighting the importance of sca…
GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs
Pu Hua, Minghuan Liu, Annabella Macaluso +4
Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes. Simulation-trained policies also face scal…
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
Guowei Xu, Ruijie Zheng, Yongyuan Liang +12
Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the…
RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
Zhecheng Yuan, Sizhe Yang, Pu Hua +3
Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization…
Simple Emergent Action Representations from Multi-Task Policy Training
Pu Hua, Yubei Chen, Huazhe Xu
The low-level sensory and motor signals in deep reinforcement learning, which exist in high-dimensional spaces such as image observations or motor torques, are inherently challengi…