7 papers
ChunkAct: Action Chunking for Agentic Robotics
Bingxin Xu, Wenrui Bao, Xinxin Liu +3
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under the guidanc…
Recursive Video In-Context Learning for Agentic Robot
Wenrui Bao, Xinxin Liu, Bingxin Xu +1
LLM agents that orchestrate frozen vision-language-action (VLA) policies improve across episodes through text memory, which records what the agent did but not how the task is done.…
DeltaWAM: Change-Centric Visual Foresight via Delta Tokens for an Efficient World-Action Model
Tianyun Jiang, Wenrui Bao, Bingxin Xu +2
World-Action Models (WAMs) offer visual foresight for robotic manipulation, but pixel-space models repeatedly reconstruct entire future scenes, incurring high computational cost an…
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…
Good Agentic Friends Do Not Just Give Verbal Advice: They Can Update Your Weights
Wenrui Bao, Huan Wang, Jian Wang +3
Multi-agent LLM systems usually collaborate by exchanging natural-language messages. This interface is simple and interpretable, but it forces each sender's intermediate computatio…
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…
Learning to Parallel: Accelerating Diffusion Large Language Models via Learnable Parallel Decoding
Wenrui Bao, Zhiben Chen, Dan Xu +1
Autoregressive decoding in large language models (LLMs) requires sequential steps for tokens, fundamentally limiting inference throughput. Recent diffusion-bas…