# Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE+UPOM

arXiv: https://arxiv.org/abs/2507.11345 · HTML: https://mlsanity.com/paper/2507.11345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects](https://mlsanity.com/paper/1809.10790/) (2018)
- [Towards Blended Reactive Planning and Acting using Behavior Trees](https://mlsanity.com/paper/1611.00230/) (2016)
- [PlanSys2: A Planning System Framework for ROS2](https://mlsanity.com/paper/2107.00376/) (2021)
- [Deliberative Acting, Online Planning and Learning with Hierarchical Operational Models](https://mlsanity.com/paper/2010.01909/) (2020)

## Abstract (arXiv)

Robotic task execution faces challenges due to the inconsistency between symbolic planner models and the rich control structures actually running on the robot. In this paper, we present the first physical deployment of an integrated actor-planner system that shares hierarchical operational models for both acting and planning, interleaving the Reactive Acting Engine (RAE) with an anytime UCT-like Monte Carlo planner (UPOM). We implement RAE+UPOM on a mobile manipulator in a real-world deployment for an object collection task. Our experiments demonstrate robust task execution under action failures and sensor noise, and provide empirical insights into the interleaved acting-and-planning decision making process.