arXiv:2604.08168v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models have advanced robot manipulation through large-scale pretraining, but real-world deployment remains challenging due to partial observability and delayed feedback. Reinforcement learning addresses this via value functions, which assess task progress and guide policy improvement. However, existing value models built on vision-language models (VLMs) struggle to capture temporal dynamics and physical interactions, undermining reliable value estimation in long-horizon tasks. In this paper, we propose ViVa,
The proliferation of large language models and vision transformers now enables the creation of more sophisticated video-generative models applicable to robotic control, addressing long-standing challenges in reinforcement learning.
Improving robot reinforcement learning through better temporal dynamics and physical interaction modeling is critical for advancing autonomous systems beyond controlled environments, reducing deployment friction and costs.
Robot learning systems can now incorporate more nuanced video-based generative models to better understand and predict outcomes in complex, real-world scenarios, enhancing reliability and long-horizon task capabilities.
- · Robotics companies
- · Logistics and manufacturing automation
- · AI research institutions
- · Companies relying on manual labor in repetitive tasks
- · Traditional robot programming methodologies
Robots will become more adept at handling unstructured environments and tasks with partial observability.
Accelerated development and adoption of AI-driven robotic solutions across various industries due to increased robustness.
Potential for a significant reduction in human intervention in complex robotic operations, leading to new economic models and workforce restructuring.
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Read at arXiv cs.AI