Embodied Reasoning
Overview
Embodied reasoning connects what an agent sees with what it knows about space, time, objects, and physical cause and effect, so it can choose actions and adapt when the world changes. Approaches include vision-language and vision-language-action models, structured scene graphs and world models, language-based reasoning, memory and reflection, and reinforcement learning. Benchmarks test navigation, manipulation, and long-horizon tasks in simulators such as ALFRED and BEHAVIOR as well as on real robots. Models are improving, but fluent plans and strong benchmark scores do not always translate into grounded, reliable action, especially under changing conditions or partial observation.
Latest papers
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