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HEROIC is a decentralized heterogeneous multi-agent open-vocabulary search coordination framework that requires agents to communicate in natural language only and reaches the target 84% of the time across all 6 scenes, compares to 35-54% for vision-language frontier baselines, frontier-based search, lawnmower, and random-walk running the same perception.
Achieving grasp synthesis in under five evaluations, this method accelerates generative models by up to 39 times while maintaining state-of-the-art performance.
COLMAR boosts multi-agent 3D reconstruction accuracy by over 50% through smarter, cooperative viewpoint selection.
By recasting the Hamilton-Jacobi-Bellman equation as a tractable Monte Carlo estimation, this work stabilizes physics-informed RL and unlocks its potential for high-dimensional control tasks.
