5 papers
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
Yi Wang, Xinchen Li, Pengwei Xie +13
Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter dist…
-WM: A Unified Video-Action World Model for Robotic Manipulation
Pengfei Zhou, Shengcong Chen, Di Chen +17
Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execution. We present -World…
Say, Dream, and Act: Learning Video World Models for Instruction-Driven Robot Manipulation
Songen Gu, Yunuo Cai, Tianyu Wang +2
Robotic manipulation requires anticipating how the environment evolves in response to actions, yet most existing systems lack this predictive capability, often resulting in errors…
RealCamo: Boosting Real Camouflage Synthesis with Layout Controls and Textual-Visual Guidance
Chunyuan Chen, Yunuo Cai, Shujuan Li +3
Camouflaged image generation (CIG) has recently emerged as an efficient alternative for acquiring high-quality training data for camouflaged object detection (COD). However, existi…
AnyRefill: A Unified, Data-Efficient Framework for Left-Prompt-Guided Vision Tasks
Ming Xie, Chenjie Cao, Yunuo Cai +3
In this paper, we present a novel Left-Prompt-Guided (LPG) paradigm to address a diverse range of reference-based vision tasks. Inspired by the human creative process, we reformula…