# Mobile Manipulation with Active Inference for Long-Horizon Rearrangement Tasks

arXiv: https://arxiv.org/abs/2507.17338 · HTML: https://mlsanity.com/paper/2507.17338/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2025.

## Built on by (most cited first, 2 of 2)

- [Natural Building Blocks for Structured World Models: Theory, Evidence, and Scaling](https://mlsanity.com/paper/2511.02091/) (2025)
- [A Hardware-oriented Approach for Efficient Bayesian Inference Computation and Deployment](https://mlsanity.com/paper/2607.17855/) (2026)

## Abstract (arXiv)

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.