5 papers
MobileWAM: Bridging World Action Models to Mobile Manipulation with Chain-of-Foresight
Zehua Fan, Junjie He, Wenxuan Song +14
World action models (WAMs) built on video generation backbones are a rising recipe for robot learning, yet remain confined to tabletop manipulation. Mobile manipulation demands sim…
PROSPECT: Unified Streaming Vision-Language Navigation via Semantic--Spatial Fusion and Latent Predictive Representation
Zehua Fan, Wenqi Lyu, Wenxuan Song +12
Multimodal large language models (MLLMs) have advanced zero-shot end-to-end Vision-Language Navigation (VLN), yet robust navigation requires not only semantic understanding but als…
Dexterous Grasping with Real-World Robotic Reinforcement Learning
Dongchi Huang, Tianle Zhang, Yihang Li +5
Dexterous grasping in the real world presents a fundamental and significant challenge for robot learning. The ability to employ affordance-aware poses to grasp objects with diverse…
CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning
Dongchi Huang, Zhirui Fang, Tianle Zhang +3
Vision-Language-Action (VLA) models demonstrate significant potential for developing generalized policies in real-world robotic control. This progress inspires researchers to explo…
Object-Focus Actor for Data-efficient Robot Generalization Dexterous Manipulation
Yihang Li, Tianle Zhang, Xuelong Wei +7
Robot manipulation learning from human demonstrations offers a rapid means to acquire skills but often lacks generalization across diverse scenes and object placements. This limita…