# Team Orienteering Coverage Planning with Uncertain Reward

arXiv: https://arxiv.org/abs/2105.03721 · HTML: https://mlsanity.com/paper/2105.03721/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2021.

## Built on by (most cited first, 4 of 4)

- [Grounded Curriculum Learning](https://mlsanity.com/paper/2409.19816/) (2024)
- [Bi-CL: A Reinforcement Learning Framework for Robots Coordination Through Bi-level Optimization](https://mlsanity.com/paper/2404.14649/) (2024)
- [Scaling Team Coordination on Graphs with Reinforcement Learning](https://mlsanity.com/paper/2403.05787/) (2024)
- [GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring](https://mlsanity.com/paper/2508.02988/) (2025)

## Abstract (arXiv)

Many municipalities and large organizations have fleets of vehicles that need to be coordinated for tasks such as garbage collection or infrastructure inspection. Motivated by this need, this paper focuses on the common subproblem in which a team of vehicles needs to plan coordinated routes to patrol an area over iterations while minimizing temporally and spatially dependent costs. In particular, at a specific location (e.g., a vertex on a graph), we assume the cost grows linearly in expectation with an unknown rate, and the cost is reset to zero whenever any vehicle visits the vertex (representing the robot servicing the vertex). We formulate this problem in graph terminology and call it Team Orienteering Coverage Planning with Uncertain Reward (TOCPUR). We propose to solve TOCPUR by simultaneously estimating the accumulated cost at every vertex on the graph and solving a novel variant of the Team Orienteering Problem (TOP) iteratively, which we call the Team Orienteering Coverage Problem (TOCP). We provide the first mixed integer programming formulation for the TOCP, as a significant adaptation of the original TOP. We introduce a new benchmark consisting of hundreds of randomly generated graphs for comparing different methods. We show the proposed solution outperforms both the exact TOP solution and a greedy algorithm. In addition, we provide a demo of our method on a team of three physical robots in a real-world environment.