arXiv AI
LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies
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TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM
Vision-language-action (VLA) models commonly adopt an LLM-centric $V \to L \to A$ pathway, where visual observations are projected into the representation space of a large language model before being decoded into robot actions. Although effective, this design incurs substantial computation and memory overhead at every policy invocation.
arXiv AI
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Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models
arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.
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CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control
arXiv:2606. 09572v1 Announce Type: cross Abstract: Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution.
arXiv Computer Vision
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V-Link: Recovering Lost Visual Representations in Action DiT for Vision-Language-Action Models
V-Link is a method designed to enhance Vision‑Language‑Action (VLA) models by recovering visual representations during the transfer from vision‑language (VL) features to action (A) features. It introduces complementary Spatial and Semantic Query representations that are injected into Action DiT through asymmetric pathways, providing both semantic augmentation and dedicated geometric conditioning for action generation. Experiments on LIBERO, LIBERO‑Plus, RoboTwin 2.0, and real‑world AGIBOT A3 Ultra tasks show significant performance gains over the base GR00T N1.6 model.
arXiv AI
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Towards Long-horizon Embodied Agents with Tool-Aligned Vision-Language-Action Models
arXiv:2605.13119v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models are effective robot action executors, but they remain limited on long-horizon tasks due to the dual burde...
arXiv AI
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Rewiring Semantics, Dynamics, and Control: A Simple yet Effective Action-Centric Tri-Stream Transformer
The paper introduces ACT^3, an Action-Centric Tri-Stream Transformer that merges semantic insights from Vision‑Language Models with dynamics predictions from World Models to generate control actions for robotic manipulation. By allowing a dedicated action expert to attend to both VLM and WM representations while keeping each backbone’s forward pass independent, ACT^3 preserves the distinct roles of context streams. Experiments on simulated and real‑world benchmarks demonstrate that ACT^3 outperforms existing approaches.