# Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems

arXiv: https://arxiv.org/abs/2510.03472 · HTML: https://mlsanity.com/paper/2510.03472/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 23 of 23)

- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelong](https://mlsanity.com/paper/2010.08184/) (2020)
- [Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding](https://mlsanity.com/paper/1901.11282/) (2019)
- [LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding](https://mlsanity.com/paper/2211.13432/) (2022)
- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries](https://mlsanity.com/paper/1904.10656/) (2019)
- [pyribs: A Bare-Bones Python Library for Quality Diversity Optimization](https://mlsanity.com/paper/2303.00191/) (2023)
- [Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding](https://mlsanity.com/paper/2112.03534/) (2021)
- [Video Game Level Repair via Mixed Integer Linear Programming](https://mlsanity.com/paper/2010.06627/) (2020)
- [Multi-Robot Coordination and Layout Design for Automated Warehousing](https://mlsanity.com/paper/2305.06436/) (2023)
- [Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2308.11234/) (2023)
- [Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers](https://mlsanity.com/paper/1912.00253/) (2019)
- [Guidance Graph Optimization for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2402.01446/) (2024)
- [Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities](https://mlsanity.com/paper/2404.16162/) (2024)
- [Arbitrarily Scalable Environment Generators via Neural Cellular Automata](https://mlsanity.com/paper/2310.18622/) (2023)
- [Online Guidance Graph Optimization for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2411.16506/) (2024)
- [Flow-Based Integrated Assignment and Path-Finding for Mobile Robot Sorting Systems](https://mlsanity.com/paper/2303.04070/) (2023)
- [Caching-Augmented Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2403.13421/) (2024)
- [Robotic Sorting Systems: Robot Management and Layout Design Optimization](https://mlsanity.com/paper/2404.04832/) (2024)
- [Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search](https://mlsanity.com/paper/2408.16890/) (2024)

## Built on by (most cited first, 3 of 3)

- [Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems](https://mlsanity.com/paper/2602.15721/) (2026)
- [Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2602.23468/) (2026)
- [Planning over MAPF Agent Dependencies via Multi-Dependency PIBT](https://mlsanity.com/paper/2603.23405/) (2026)

## Abstract (arXiv)

We study optimizing a destination-to-chutes task mapping to improve throughput in Robotic Sorting Systems (RSS), where a team of robots sort packages on a sortation floor by transporting them from induct workstations to eject chutes based on their shipping destinations (e.g. Los Angeles or Pittsburgh). The destination-to-chutes task mapping is used to determine which chutes a robot can drop its package. Finding a high-quality task mapping is challenging because of the complexity of a real-world RSS. First, optimizing task mapping is interdependent with robot target assignment and path planning. Second, chutes will be CLOSED for a period of time once they receive sufficient packages to allow for downstream processing. Third, task mapping quality directly impacts the downstream processing, as scattered chutes for the same destination increase package handling time. In this paper, we first formally define task mappings and the problem of Task Mapping Optimization (TMO). We then present a simulator of RSS to evaluate task mappings. We then present a simple TMO method based on the Evolutionary Algorithm and Mixed Integer Linear Programming, demonstrating the advantage of our optimized task mappings over the greedily generated ones in various RSS setups with different map sizes, numbers of chutes, and destinations. Finally, we use Quality Diversity algorithms to analyze the throughput of a diverse set of task mappings. Our code is available online at https://github.com/lunjohnzhang/tmo_public.