28 citations · 29 across the 6 of their papers we have counts for
6 papers
H-VLA: Hierarchical Vision-Language-Action Model with Key-Action Reasoning and Motion Planning in a Unified Action Space
Xiongfeng Peng, Lu Xu, Yandong Wang +10
Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but many existing methods still rely on direct mappings from language and visual observati…
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…
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
Xiangjun Wang, Junxiao Song, Penghui Qi +9
AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy…