1 citations · 1 across the 5 of their papers we have counts for
5 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…
Understanding LoRA as Knowledge Memory: An Empirical Analysis
Seungju Back, Dongwoo Lee, Naun Kang +4
Continuous knowledge updating for pre-trained large language models (LLMs) is increasingly necessary yet remains challenging. Although inference-time methods like In-Context Learni…
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…