4 papers
Don't Drop the BATON: Long-Horizon Robot Manipulation via Agentic Subtask Exploration and Transition-aware Memory
Bingxin Xu, Yuzhang Shang, Emilio Ferrara
Long-horizon robot manipulation chains many contact-rich skills into one multi-stage task. Vision-language-action (VLA) models increasingly master the individual skills, yet the ch…
Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning
Wenrui Bao, Tianyun Jiang, Zhiben Chen +3
Learning reliable surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated surgical robot (e.g., dVRK) trajectories with synchr…
VLA-Thinker: Boosting Vision-Language-Action Models through Thinking-with-Image Reasoning
Chaoyang Wang, Wenrui Bao, Sicheng Gao +5
Vision-Language-Action (VLA) models have shown promising capabilities for embodied intelligence, but most existing approaches rely on text-based chain-of-thought reasoning where vi…
ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms
Bingxin Xu, Zhen Dong, Oussama Elachqar +1
Large language models require massive memory footprints, severely limiting deployment on consumer hardware. Quantization reduces memory through lower numerical precision, but extre…