arXiv AI By Loan Bernat (LAAS-GEPETTO), Matthieu Grard (LAAS-RAP), Ariane Herbulot (LAAS-RAP), Florent Lamiraux (LAAS-GEPETTO)

MAGMA-Bench: Evaluating Long-Horizon Embodied Agents under Dynamic Tasks and Execution Failures

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MAGMA-Bench is an architecture‑agnostic benchmark designed to evaluate long‑horizon embodied agents in dynamic multi‑robot environments. It tests agents across increasing task horizons and introduces controlled online perturbations such as execution failures, task interruptions, and mission updates, while requiring coordination among robots with heterogeneous capabilities. The benchmark separates task‑level reasoning from perception and low‑level control through a structured skill‑level interface and provides 261,646 task‑level training examples along with an expert‑trace generation pipeline.

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