Yulun: Add TMO paper; Add additional links in earlier publications

_data/pubs.yml

@@ -30,6 +30,29 @@
 #  abstract: null
 
 ############### 2025 ##################
+- key: ZhangMRS25
+  title: Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems
+  site: https://yulunzhang.net/publication/zhang2025tmo/
+  authors: [Yulun Zhang, Alexandre O. G. Barbosa, Federico Pecora, Jiaoyang Li]
+  equal_contributions: [] # Authors with asterisks
+  venue: MRS
+  volume: null
+  number: null
+  pages: null
+  year: 2025
+  thumbnail: /files/yulunzhang/thumbnails/task-mapping.png
+  award: null
+  doi: null  # doi is shown in bibtex and used as the link to the publisher site when the publisher field is not defined
+  publisher: null  # link to the publisher; we don't need this if the publisher can be reached by https://doi.org/[doi] 
+  tags: [mapf, warehouse, envopt]
+  links: # You can add additional links not listed below 
+    arXiv: https://arxiv.org/abs/2510.03472
+    Code: https://github.com/lunjohnzhang/tmo_public
+    Poster: null
+    Slides: https://1drv.ms/p/c/6847b8d033285874/IQAin2Q66i_kS585KHS8oI4qAb7MY8dRl8pFClk9XKe2PLo?e=la1F03
+    Talk: null
+  abstract: 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.
+
 
 - key: HuangIROS25
   title: Benchmarking Shortcutting Techniques for Multi-Robot-Arm Motion Planning
@@ -312,11 +335,15 @@
   venue: AAAI
   pages: 14726-14735
   year: 2025
+  thumbnail: /files/yulunzhang/thumbnails/online_ggo_pipeline_pibt.png
   doi: 10.1609/aaai.v39i14.33614
   tags: [warehouse, envopt]
   links:
     arXiv: https://arxiv.org/abs/2411.16506
     Code: https://github.com/zanghz21/OnlineGGO
+    Slides: https://1drv.ms/p/c/6847b8d033285874/ERkuhW00_gZIjOZTdimgkmsB6VJpSYd3cytN_WjWjJkVMg?e=r2GSqO
+    Poster: https://drive.google.com/file/d/1Cwy9v44roCINx8Kry6aGkYrkUwaQM0FD/view?usp=drive_link
+    Talk: https://drive.google.com/file/d/1d35gmt18vlJ3XKdJwzmxlKv9JeASLLsc/view?t=291
   abstract: We study the problem of optimizing a guidance policy capable of dynamically guiding the agents for lifelong Multi-Agent Path Finding based on real-time traffic patterns. Multi-Agent Path Finding (MAPF) focuses on moving multiple agents from their starts to goals without collisions. Its lifelong variant, LMAPF, continuously assigns new goals to agents. In this work, we focus on improving the solution quality of PIBT, a state-of-the-art rule-based LMAPF algorithm, by optimizing a policy to generate adaptive guidance. We design two pipelines to incorporate guidance in PIBT in two different ways. We demonstrate the superiority of the optimized policy over both static guidance and human-designed policies. Additionally, we explore scenarios where task distribution changes over time, a challenging yet common situation in real-world applications that is rarely explored in the literature.
 
 
@@ -396,6 +423,8 @@
   links:
       arXiv: https://arxiv.org/abs/2402.01446
       Code: https://github.com/lunjohnzhang/ggo_public
+      Slides: https://1drv.ms/p/c/6847b8d033285874/EZq5Q0-gmAdMvCXkIYwLsckB2fjowNeo6jFClNztvg7OUw?e=ZkKSAC
+      Poster: https://drive.google.com/file/d/1aC1tdt7oj-8d4ZimtV1BBLPy8SCDEkxs/view?usp=sharing
   abstract: We study how to use guidance to improve the throughput of lifelong Multi-Agent Path Finding (MAPF). Previous studies have demonstrated that, while incorporating guidance, such as highways, can accelerate MAPF algorithms, this often results in a trade-off with solution quality. In addition, how to generate good guidance automatically remains largely unexplored, with current methods falling short of surpassing manually designed ones. In this work, we introduce the guidance graph as a versatile representation of guidance for lifelong MAPF, framing Guidance Graph Optimization as the task of optimizing its edge weights. We present two GGO algorithms to automatically generate guidance for arbitrary lifelong MAPF algorithms and maps. The first method directly optimizes edge weights, while the second method optimizes an update model capable of generating edge weights. Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in eight benchmark maps, and (2) our update model can generate guidance graphs for as large as $93 \times 91$ maps and as many as 3,000 agents.
 
 - key: FriedrichIJCAI24
@@ -584,6 +613,8 @@
   links:
     arXiv: https://arxiv.org/abs/2310.18622
     Code: https://github.com/lunjohnzhang/warehouse_env_gen_nca_public
+    Slides: https://1drv.ms/p/s!AnRYKDPQuEdokj2ML5zeupgxfHeS?e=7DQeFp
+    Poster: https://drive.google.com/file/d/117-DPRB0pQqmxogUoVdfR2ewQRtCoWrf/view?usp=sharing
     Talk: https://slideslive.com/39008681
   abstract: We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases. Additionally, the previous methods have only been tested with up to 350 robots in simulations, while practical warehouses could host thousands of robots. In this paper, instead of optimizing environments, we propose to optimize Neural Cellular Automata (NCA) environment generators via QD algorithms. We train a collection of NCA generators with QD algorithms in small environments and then generate arbitrarily large environments from the generators at test time. We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2,350 robots. Additionally, we demonstrate that our method scales a single-agent reinforcement learning policy to arbitrarily large environments with similar patterns.
 
@@ -632,6 +663,8 @@
   links:
     arXiv: https://arxiv.org/abs/2305.06436
     Code: https://github.com/lunjohnzhang/warehouse_env_gen_public
+    Slides: https://1drv.ms/p/s!AnRYKDPQuEdokjwgGQmvBt9ea2Th?e=kFquJv
+    Poster: https://drive.google.com/file/d/1VAzhyZHDgp-eHlGQNDSdq-zWDhgAVxQl/view?usp=drive_link
   abstract: With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehouses. While most works try to improve the throughput of such warehouses by developing better MAPF algorithms, we focus on improving the throughput by optimizing the warehouse layout. We show that, even with state-of-the-art MAPF algorithms, commonly used human-designed layouts can lead to congestion for warehouses with large numbers of robots and thus have limited scalability. We extend existing automatic scenario generation methods to optimize warehouse layouts. Results show that our optimized warehouse layouts (1) reduce traffic congestion and thus improve throughput, (2) improve the scalability of the automated warehouses by doubling the number of robots in some cases, and (3) are capable of generating layouts with user-specified diversity measures.
 
 - key: LamICAPS23