3 papers
cs.RO2026
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations
Yizhou Chen, Hang Xu, Dongjie Yu +7
Successfully automating dexterous, long-horizon robotic manipulation requires frameworks capable of both high-level reasoning and fine-grained execution. Traditional task and motio…
cs.RO2026
SSI-Policy: Learning Structured Scene Interfaces for Vision-Language Robotic Manipulation
Kaijun Wang, Zikai Ouyang, Xuping Wu +6
Real-world robotic manipulation demands spatial grounding, task-aware reasoning, and precise control. Learning such capabilities becomes particularly challenging in the low-data re…
cs.RO2026
MinInter: Minimizing Trajectory Interpolation During Data Augmentation for Imitation Learning
Qingyang Wang, Xingang Liu, Changwei Yao +4
Imitation learning enables robots to acquire complex manipulation skills from demonstrations, but its effectiveness is limited by the cost of collecting high-quality data. Trajecto…