arXiv AI
SUN: Persistent Programs For Language-Grounded Control-to-Learning-to-Real Policies
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arXiv AI
Jul 7
DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation
arXiv:2607. 04927v1 Announce Type: cross Abstract: World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense supervision for robot action learning.
arXiv AI
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EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents
EmbodiedSkills is a unified framework that treats each skill decision as an execution proposal, checking prerequisites and verifying outcomes during long‑horizon vision‑language‑action tasks. It connects high‑level skill selection, bounded low‑level VLA execution, and post‑action verification through a fixed executable‑skill interface, enabling easy replacement of low‑level policies and recording of structured trajectories for supervision and adaptation. Instantiated with Qwen3‑VL and OpenPI/pi0.5 on RoboTwin 2.0 and LIBERO, the framework achieves high success rates (86.20% and 97.40% respectively) and demonstrates effective memory‑dependent task performance.
arXiv AI
Jun 30
Demonstration-Free Robotic Control via LLM Agents
arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.
arXiv AI
Jul 3
CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
arXiv:2603. 22435v2 Announce Type: replace-cross Abstract: "Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as autonomous controllers for embodied manipulation remains underexplored.
arXiv AI
Jul 1
Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models
arXiv:2606. 31846v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and continuous control.
arXiv AI
Jun 12
LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories
arXiv:2606. 13578v1 Announce Type: cross Abstract: Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach.