Mobile Manipulation with Active Inference for Long-Horizon Rearrangement Tasks

Corrado Pezzato, Ozan Catal, Toon Van de Maele, Riddhi J. Pitliya, Tim Verbelen

Keywords: active inference, variational inference, habitat, control as inference, planning as inference

TL;DR: A hybrid discrete-continuous generative model enabling long-horizon robotic tasks with active inference.

Abstract: Despite growing interest in active inference for robotic control, its application to complex, long-horizon tasks remains untested. We address this gap by introducing a fully hierarchical active inference architecture for goal-directed behavior in realistic robotic settings. Our model combines a high-level active inference model that selects among discrete skills realized via a whole-body active inference controller. This unified approach enables flexible skill composition, online adaptability, and recovery from task failures without requiring offline training. Evaluated on the Habitat Benchmark for mobile manipulation, our method outperforms state-of-the-art baselines across the three long-horizon tasks, demonstrating for the first time that active inference can scale to the complexity of modern robotics benchmarks.

Submission Number: 67