Projects

Selected work on human-in-the-loop robot learning, manipulation, and robust control. Open each project website for videos, results, and code.

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.

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Novel Demonstration Generation with Gaussian Splatting Enables Robust One-Shot Manipulation

Sizhe Yang^*, Wenye Yu^*, Jia Zeng, Jun Lv, Kerui Ren, Cewu Lu, Dahua Lin, Jiangmiao Pang^†

Robotics: Science and Systems (RSS) · 2025

RoboSplat is framework that leverages 3D Gaussian Splatting (3DGS) to generate novel demonstrations for RGB-based policy learning in a one-shot manner, enabling robust performance across six types of visual generalization in the real world.

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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.

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