Skel-WAM is introduced, a world action model that bridges differences between human and robot motion through a unified hand-skeleton motion interface, and demonstrates that a shared skeletal interface enables joint learning across human and robot data and expands robot task coverage through complementary human demonstr...
Ze-Tao Cai, Ya-Ping Li, Yi-Qun Wang et al.· 0 citations
This work presents RoboInter1.5, an extended and holistic suite of intermediate representations for both robotic manipulation and embodied world modeling, and introduces RoboInter-World, which leverages intermediate representations as structured conditioning signals for controllable prediction of future world states.
Ziqin Wang, Hao Li, Weijun Wang et al.· arXiv.org· 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.
This work presents REAL, an agentic framework for open-world mobile manipulation, which establishes sim-to-real-consistent environment APIs without oracle perception and integrates a simulated user to enable human-in-the-loop interaction.
Boyu Mi, Mengchen Ma, Yifei Yao et al.· arXiv.org· 2 citations
Cortex is introduced, a bidirectionally aligned embodied agent framework with a customized planning interface that conveys executable and tractable subtask plans from high-level VLM to low-level VLA and enables zero-shot completion of unseen real-world long-horizon tasks.
Jiaqi Peng, Xiqian Yu, Delin Feng et al.· 1 citation
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