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continual learning 26 reinforcement learning 9 language models 5 large language models 4 robotics 4 agents 3 generative models 3 multi-agent systems 3 recursive self-improvement 3 vision-language models 3

16 SEP 2026 · Paper

In-Context Robot Learning with VLM Agents

This paper develops a framework for robots to learn from context without relying on pre-programmed demonstrations, allowing them to adapt to new environments. Practitioners might care because this technology could enable robots to perform tasks more efficiently and effectively in real-world situations.

embodied AI vision-language models continual learning robot learning robotics

14 SEP 2026 · Paper

PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models

This paper develops a unified model that can understand physical environments, generate actions, and predict future states, using a combination of vision, language, and embodied interactions. Practitioners may care about this model because it could be used to create robots or other agents that can interact with and adapt to their physical surroundings.

vision-language models physical foundation models embodied AI multimodal learning robotics