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Assessing the Marginal Utility of Exocentric Head Views in UMI-Style Large-FoV Wrist-Camera Manipulation
Yuhui Duan, Shi Jin, Yuntian Wang +5
Multi-view perception is often treated as a safe design choice for robot manipulation: if one camera is useful, more cameras should be better. We revisit that assumption in tableto…
ARP: Enhancing Quantized Skill Abstractions via Visual Alignment and Iterative Refinement for Robotic Manipulation
Yuntian Wang, Zesheng Jia, Yuhui Duan +5
Learning visuomotor policies for long-horizon manipulation remains a fundamental challenge. Recent skill-based imitation learning methods based on discrete quantization have shown…
GUIDE: Goal-Initialized Directional Understanding for End-to-End Legged Navigation
Liang Wang, Jin Jin, KanZhong Yao +6
End-to-end reinforcement learning (RL) has shown strong potential for legged robot navigation, yet existing approaches commonly rely on continuously updated robot-to-goal states fr…
UMI-Bench 1.0: An Open and Reproducible Real-World Benchmark for Tabletop Robotic Manipulation with UMI Data
Shi Jin, Yuntian Wang, Yuhui Duan +16
Real-robot evaluation is essential for understanding whether learned manipulation policies can operate reliably outside curated demonstrations. This need is particularly pressing f…
Move What Matters: Parameter-Efficient Domain Adaptation via Optimal Transport Flow for Collaborative Perception
Zesheng Jia, Jin Wang, Siao Liu +4
Efficient domain adaptation remains a fundamental challenge for deploying multi-agent systems across diverse environments in Vehicle-to-Everything (V2X) collaborative perception. D…