Single-stage fine-tuning of a video model to generate actions as latent frames plus future states and values yields state-of-the-art robot policy performance on LIBERO, RoboCasa, and bimanual tasks.
A dual process vla: Efficient robotic manipulation leveraging vlm.arXiv preprint arXiv:2410.15549
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Action-state consistency in World Action Models distinguishes successful from failed imagined futures and supports value-free selection of better rollouts via consensus among predictions.
The Semantic Autonomy Stack combines a seven-step parametric resolver handling 88% of instructions in under 0.1 ms with VLM escalation and a five-category cross-robot memory system, achieving 100% accuracy and 103,000-fold latency reduction on Raspberry Pi 5 robots with no GPU or training data.
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Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
Single-stage fine-tuning of a video model to generate actions as latent frames plus future states and values yields state-of-the-art robot policy performance on LIBERO, RoboCasa, and bimanual tasks.
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Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models
Action-state consistency in World Action Models distinguishes successful from failed imagined futures and supports value-free selection of better rollouts via consensus among predictions.
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A Semantic Autonomy Framework for VLM-Integrated Indoor Mobile Robots: Hybrid Deterministic Reasoning and Cross-Robot Adaptive Memory
The Semantic Autonomy Stack combines a seven-step parametric resolver handling 88% of instructions in under 0.1 ms with VLM escalation and a five-category cross-robot memory system, achieving 100% accuracy and 103,000-fold latency reduction on Raspberry Pi 5 robots with no GPU or training data.