World Action Models (WAMs) jointly model visual dynamics and action generation for generalist robot manipulation. A central challenge is to integrate priors from large-scale pretrained models---including visual dynamics, scene semantics, geometry, and motion---into a unified framework for robot action generation. We in...
Xing-Yu Miao, Zi-Zun Li, Bao-Le Fang et al.· 0 citations
Advantage-guided reinforcement learning provides a practical way to post-train vision-language-action (VLA) policies using limited robot data. However, its performance depends on several coupled choices, including how critic-derived advantages are constructed, calibrated, and used for policy training. Existing recipes...
Jia-Hang Cao, Han-Ye Zhao, Hang Lai et al.· 0 citations
Physical intelligence requires more than predicting how the world may evolve: predictions must remain actionable as the world continues to change. We introduce InternW0, the first instantiation of the InternW physical world model series from Shanghai AI Laboratory, built around omnimodal interfaces, asynchronous multi-...
Ji-Song Cai, Yao Mu, Gan-Lin Yang et al.· 0 citations
InternVLA-A1.5 is presented, which builds the policy on a native VLM backbone that keeps training on VQA and subtask prediction, and attaches a lightweight unified expert for continuous action generation, and achieves the best overall results on all six simulation benchmarks.
