GesVLA encodes gesture features directly into the latent space of VLA models using a dual-VLM architecture and a rendering-based data pipeline, yielding improved target grounding in real robotic tasks.
A dual process vla: Efficient robotic manipulation leveraging vlm
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
The paper quantifies the geometric gap in current VLAs via linear probing and compares three architectures for injecting geometry from GFMs while analyzing impacts of data, cameras, and reconstruction quality.
citing papers explorer
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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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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.