Pretraining navigation diffusion policies rely on large-scale expert demonstrations. These data are typically generated by a fully-informed oracle planner suited to a single nominal robot. This limits the policy's generalization to diverse embodiments and challenging scenarios (e.g., escaping dead ends or detouring lon...
Tian-Yu Yang, Yiming Zeng, Wenzhe Cai 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.
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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