Publications

Research in robotics and embodied AI.

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* Equal contribution. Other author markers follow the original papers

2026

Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection

Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection

Yi Wang*, Wendi Chen*‡, Zimo Wen*, Han Xue, Xueqi Li, Wenye Yu, Zhijie Chen, Hao Yang, Jun Lv, Chuan Wen, Cewu Lu

Conference on Robot Learning (CoRL) · 2026

LIFT adds reactive force feedback to pretrained vision-language-action policies while preserving their general manipulation knowledge. By combining causal force memory with online DAgger corrections, it accelerates post-training and improves performance on contact-rich manipulation tasks.

ARMADA: Autonomous Online Failure Detection and Human Shared Control Empower Scalable Real-world Deployment and Adaptation

Wenye Yu, Jun Lv, Zixi Ying, Yang Jin, Chuan Wen, Cewu Lu

IEEE Robotics and Automation Letters (RA-L) · 2026 · Best Paper Award at IROS 2025 HRII Workshop

ARMADA is a multi-robot deployment and adaptation system with human-in-the-loop shared control, featuring an autonomous online failure detection method named FLOAT. It enables scalable deployment of pretrained policies and thereby expedites policy adaptation to novel scenarios.

2025

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration

SIME: Enhancing Policy Self-Improvement with Modal-level Exploration

Yang Jin*, Jun Lv*, Wenye Yu, Hongjie Fang, Yong-Lu Li, Cewu Lu

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · 2025

With modal-level exploration, the robot can generate more diverse and multi-modal interaction data. By learning from the most valuable trials and high-quality segments from these interactions, the robot can effectively refine its capabilities through self-improvement.

2024

Learning H-Infinity Locomotion Control

Learning H-Infinity Locomotion Control

Junfeng Long*, Wenye Yu*, Quanyi Li*, Zirui Wang, Dahua Lin, Jiangmiao Pang

Conference on Robot Learning (CoRL) · 2024 · Best Poster Award at CoRL 2024 LocoLearn Workshop

We propose H-Infinity Locomotion Control, an adversarial framework for quadrupedal robots which enhances their robustness against external forces with a performance guarantee.

GRUtopia: Dream General Robots in a City at Scale

GRUtopia: Dream General Robots in a City at Scale

Hanqing Wang*, Jiahe Chen*, Wensi Huang*, Qingwei Ben*, Tai Wang*, Boyu Mi*, Tao Huang, Siheng Zhao, Yilun Chen, Sizhe Yang, Peizhou Cao, Wenye Yu, Zichao Ye, Jialun Li, Junfeng Long, Zirui Wang, Huiling Wang, Ying Zhao, Zhongying Tu, Yu Qiao, Dahua Lin, Jiangmiao Pang

arXiv · 2024

We proposed GRUtopia, the first simulated interactive 3D society designed for various robots. It features (a)GRScenes, a dataset with 100k interactive and finely annotated scenes. (b) GRResidents, a LLM driven NPC system. (c) GRBench, a benchmark posing moderately challenging tasks.