57 citations · 170 across the 34 of their papers we have counts for
27 papers · 1 filter
ITA-LaCAM: A Complete and Scalable TAPF Solver via Assignment-Aware Configuration-Space Search
Yimin Tang, Han Zhang, Shao-Hung Chan +4
Combined Target Assignment and Path Finding (TAPF) requires assigning targets for agents while simultaneously planning collision-free paths. We present ITA-LaCAM, a complete and sc…
From LLM-Generated Specifications to Learned Quadruped Locomotion
Merve Atasever, Keyan Azbijari, Cagan Bakirci +5
Quadruped robot locomotion policies are often trained using reinforcement learning, which in turn relies heavily on hand-crafted reward functions. Designing reward functions requir…
Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
Anthony Liang, Yigit Korkmaz, Jiahui Zhang +14
General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local, frame-level supervision. While effecti…
SyncTwin: Fast Digital Twin Construction and Synchronization for Safe Robotic Manipulation
Ruopeng Huang, Boyu Yang, Wenlong Gui +3
Accurate and safe robotic manipulation under dynamic and visually occluded conditions remains a core challenge in real-world deployment. We introduce SyncTwin, a novel digital twin…
Judgelight: Trajectory-Level Post-Optimization for Multi-Agent Path Finding via Closed-Subwalk Collapsing
Yimin Tang, Sven Koenig, Erdem Bıyık
Multi-Agent Path Finding (MAPF) is an NP-hard problem with applications in warehouse automation and multi-robot coordination. Learning-based MAPF solvers offer fast and scalable pl…
AutoFocus-IL: VLM-based Saliency Maps for Data-Efficient Visual Imitation Learning without Extra Human Annotations
Litian Gong, Fatemeh Bahrani, Yutai Zhou +3
AutoFocus-IL is a simple yet effective method to improve data efficiency and generalization in visual imitation learning by guiding policies to attend to task-relevant features rat…