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arXiv:2609.07047v1 Announce Type: cross Abstract: Robotic manipulation often requires acting on information that is no longer visible, yet Vision-Language-Action policies are usually evaluated when t...
arXiv:2609.22854v1 Announce Type: cross Abstract: Vision-language-action models often predict actions from only the current observation, which can leave tasks involving object occlusion or visually i...
arXiv:2608.22869v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have leveraged internet-scale pretraining and task-focused finetuning to achieve strong performance on long...
The paper introduces rMuscle, a real‑time Vision‑Language‑Action inference framework that mimics human muscle memory to accelerate robotic decision making. By exploiting repeated task similarity, rMuscle uses a dual‑phase cache: a Context Cache reuses visual‑token outputs and an Action Cache reuses neuron activation patterns, reducing computation and weight accesses. Experiments on RTX 4090 and Jetson Thor show 1.29–1.42× speedups on LIBERO, RoboTwin, and physical manipulation tasks while preserving success rates on real robots.
Factory work is a promising early scenario for embodied AI: assigning repetitive manual jobs to robots has clear economic payoff, and a structured station keeps the jobs tractable for current policies...
The paper introduces 2AM, a system that separates memory and action execution in long‑horizon robot manipulation. 2AM stores task memory exclusively in a multimodal Agent, while a single RGB‑based, stateless Action Model performs motion based on language and optional 2D hints. On the LIBERO‑Mem benchmark, 2AM achieves 76.3% average completion—over 61 points higher than the best baseline—demonstrating that agent‑side memory and precise steering of the Action Model can substantially improve performance.