Chao Liang, Ke Chen, Fengxiaoxiao Li, Shibo Huang, Yufeng Sun, Zhaokai Yin, Jianhuan Lai, Haozhe Feng (AutoLab) · Agentic Robot Control

Toward the Era of Agentic Embodied Intelligence

Introduces A2-MEAS, an embodied multi-agent framework that uses learned correction codes to adapt robot policies, and A2-WLA, a vision-language-action policy with motion-aware history and a latent world model. Reports 0.6411 Q-score and 45.40% task success on all 100 BEHAVIOR 2026 public tasks, 93.98% average success on RoboTwin 2.0, and 57.92% versus 26.67% for pi0.5 across 12 real-robot tasks. The authors say they will release the system, models, training scripts and infrastructure.