5 papers
FATE: Closed-Loop Feasibility-Aware Task Generation with Active Repair for Physically Grounded Robotic Curricula
Bingchuan Wei, Bingqi Huang, Jingheng Ma +2
Recent breakthroughs in generative simulation have harnessed Large Language Models (LLMs) to generate diverse robotic task curricula, yet these open-loop paradigms frequently produ…
MoRL: Reinforced Reasoning for Unified Motion Understanding and Generation
Hongpeng Wang, Zeyu Zhang, Wenhao Li +1
Human motion understanding and generation are crucial for vision and robotics but remain limited in reasoning capability and test-time planning. We propose MoRL, a unified multimod…
MobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots
Ting Huang, Dongjian Li, Rui Yang +3
Grounding natural-language instructions into continuous control for quadruped robots remains a fundamental challenge in vision language action. Existing methods struggle to bridge…
EvoVLA: Self-Evolving Vision-Language-Action Model
Zeting Liu, Zida Yang, Zeyu Zhang +1
Long-horizon robotic manipulation remains challenging for Vision-Language-Action (VLA) models despite recent progress in zero-shot generalization and simulation-to-real-world trans…
VLA-R1: Enhancing Reasoning in Vision-Language-Action Models
Angen Ye, Zeyu Zhang, Boyuan Wang +3
Vision-Language-Action (VLA) models aim to unify perception, language understanding, and action generation, offering strong cross-task and cross-scene generalization with broad imp…