5 papers
Co-VLA: Coordination-Aware Structured Action Modeling for Dual-Arm Vision-Language-Action Systems
Yandong Wang, Jiaqian Yu, Xiongfeng Peng +8
Vision-language-action (VLA) models show strong capabilities in single and dual-arm robotic manipulation. Prior works show coordinated bimanual behaviors can emerge from end-to-end…
Mem-World: Memory-Augmented Action-Conditioned World Models for Persistent Robot Manipulation
Zirui Zheng, Jiaqian Yu, Xiongfeng Peng +7
Action-conditioned world models have emerged as a promising paradigm for robot learning, offering a scalable alternative to costly real-world experimentation by generating action-c…
DAM-VLA: A Dynamic Action Model-Based Vision-Language-Action Framework for Robot Manipulation
Xiongfeng Peng, Jiaqian Yu, Dingzhe Li +8
In dynamic environments such as warehouses, hospitals, and homes, robots must seamlessly transition between gross motion and precise manipulations to complete complex tasks. Howeve…
MoSE: Skill-by-Skill Mixture-of-Experts Learning for Embodied Autonomous Machines
Lu Xu, Jiaqian Yu, Xiongfeng Peng +7
To meet the growing demand for smarter, faster, and more efficient embodied AI solutions, we introduce a novel Mixture-of-Expert (MoE) method that significantly boosts reasoning an…
HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction
Yi Zhou, Hui Zhang, Jiaqian Yu +4
Vectorized High-Definition (HD) map construction requires predictions of the category and point coordinates of map elements (e.g. road boundary, lane divider, pedestrian crossing,…