VLAct introduces a representation-centric continued pre-training framework for Vision-Language-Action models, achieving 92.5% on RoboTwin 2.0 and surpassing all World Action Models on RoboDojo using only 16 GPUs and open data; with 20% downstream data it outperforms GR00T N1.6 on unseen GR-1 robot.
The paper introduces the World‑Language‑Action (WLA) model, which replaces pixel‑level world‑action predictions with combined textual intent and fine‑grained physical dynamics, achieving 2 B‑parameter real‑time inference at 40 ms, doubling success rates on the RMBench benchmark and outperforming prior WAM and VLA baselines in simulation and real‑robot tests.
The new π0.7 model from Physical Intelligence demonstrates emergent compositional generalization and cross‑embodiment transfer in visual‑language‑action (VLA) robots by leveraging massive heterogeneous data and richly structured prompts, outperforming specialist Recap models on tasks such as air‑fryer cooking, clothing folding, and coffee making.
