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Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and pursue goals over many steps, raising the ceiling on what an embodied agent can understand and decide. Physical interaction, however, remains the domain of action policies, w...
Experiments demonstrate strong 3D perception and driving scene understanding while largely preserving general vision-language capability and comprehensive evaluations across open-loop, pseudo-closed-loop, and closed-loop settings further show highly competitive motion-planning performance.
NavMCP is introduced, an agentic scaffolding framework that couples a VLM reasoning agent with an NFM executor for long-horizon exploration that achieves state-of-the-art results on HM-EQA, MT-HM3D, and EXPRESS-Bench.
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