2 citations · 4 across the 5 of their papers we have counts for
5 papers
Outcome-Conditioned End-Effector Geometry Across Vision-Language-Action Policies
Xingyu Lin, Zhuang Li, Zhongrun Wu +2
Vision-language-action (VLA) policies solve the same manipulation task through different action interfaces, but task success alone does not establish whether their physical executi…
Collapse, Not Complexity: Failure-Conditioned Decomposition Repair for End-to-End Document Parsing
Xingyu Lin, Dehui Du
End-to-end document parsers increasingly offer an optional reasoning mode for complex pages. On a 180-page entropy-stratified discovery sample with one frozen 4B checkpoint, comple…
RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulation
Philipp Wu, Yide Shentu, Qiayuan Liao +5
Learning from human demonstration is an effective approach for learning complex manipulation skills. However, existing approaches heavily focus on learning from passive human demon…
HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation
Carmelo Sferrazza, Dun-Ming Huang, Xingyu Lin +2
Humanoid robots hold great promise in assisting humans in diverse environments and tasks, due to their flexibility and adaptability leveraging human-like morphology. However, resea…
Any-point Trajectory Modeling for Policy Learning
Chuan Wen, Xingyu Lin, John So +4
Learning from demonstration is a powerful method for teaching robots new skills, and having more demonstration data often improves policy learning. However, the high cost of collec…