8 papers
RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
Tianxing Chen, Yue Chen, Zixuan Li +41
Generalist robot manipulation policies have advanced rapidly, yet existing benchmarks remain limited in systematically evaluating their capabilities. Many rely on simple, short-hor…
HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
Taowen Wang, Zikang Xie, Bin Yang +13
Humanoid robots promise whole-body interaction in human-centered environments, but scalable policy learning remains difficult because task-level decision-making and whole-body dyna…
GPU-Parallel Multi-Task Reinforcement Learning with Demonstration Guided Policy Optimization
Rui Zhang, Qiwei Wu, Zhengyu Zhang +5
Large scale GPU-parallel reinforcement learning has changed what can be trained in robot simulation, yet most systems still optimize one specialist policy per task. We propose a co…
Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language
Qiwei Wu, Rui Zhang, Xin Xiang +4
Tactile sensing is essential for robots to achieve human-like gentle manipulation. However, existing Vision-Language-Action (VLA) models struggle to exploit tactile feedback for ge…
OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation
Yunyang Mo, Jian Li, Qiwei Wu +2
While reinforcement learning (RL) enables robots to acquire skills autonomously, its real-world deployment is severely limited by inefficient and unsafe exploration. Human-in-the-l…
Morphology-Consistent Humanoid Interaction through Robot-Centric Video Synthesis
Weisheng Xu, Jian Li, Yi Gu +12
Equipping humanoid robots with versatile interaction skills typically requires either extensive policy training or explicit human-to-robot motion retargeting. However, learning-bas…