Add SMART and ICAPS26

_data/people.yml

@@ -42,9 +42,9 @@
 
 - name: Jingtian Yan
   role: phd
-  webpage: https://scholar.google.com/citations?user=JjaOG98AAAAJ&hl=en
+  webpage: https://jingtianyan.github.io/
   enrollment: RI PhD, Fall 2024
-  headshot: /photos/members/jingtianyan.jpg
+  headshot: /photos/members/jingtianyan.png
   bio: Jingtian received a B.Sc. from Zhejiang University in 2020 and an M.Sc. from the Carnegie Mellon University in 2023. 
     His research interests include multi-robot coordination, autonomous exploration, and Multi-Agent Path Finding.
 

_data/preprints.yml

@@ -60,19 +60,3 @@
     Code: https://github.com/smart-mapf/lifelong-smart
   abstract: "We present Lifelong Scalable Multi-Agent Realistic Testbed (LSMART), an open-source simulator to evaluate any Multi-Agent Path Finding (MAPF) algorithm in a Fleet Management System (FMS) with Automated Guided Vehicles (AGVs). MAPF aims to move a group of agents from their corresponding starting locations to their goals. Lifelong MAPF (LMAPF) is a variant of MAPF that continuously assigns new goals for agents to reach. LMAPF applications, such as autonomous warehouses, often require a centralized, lifelong system to coordinate the movement of a fleet of robots, typically AGVs. However, existing works on MAPF and LMAPF often assume simplified kinodynamic models, such as pebble motion, as well as perfect execution and communication for AGVs. Prior work has presented SMART, a software capable of evaluating any MAPF algorithms while considering agent kinodynamics, communication delays, and execution uncertainties. However, SMART is designed for MAPF, not LMAPF. Generalizing SMART to an FMS requires many more design choices. First, an FMS parallelizes planning and execution, raising the question of when to plan. Second, given planners with varying optimality and differing agent-model assumptions, one must decide how to plan. Third, when the planner fails to return valid solutions, the system must determine how to recover. In this paper, we first present LSMART, an open-source simulator that incorporates all these considerations to evaluate any MAPF algorithms in an FMS. We then provide experiment results based on state-of-the-art methods for each design choice, offering guidance on how to effectively design centralized lifelong AGV Fleet Management Systems. LSMART is available at this https URL."
 
-
-- key: Yan2025SMART
-  title: "Advancing MAPF towards the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)"
-  site: https://jingtianyan.github.io/publication/2025-03-02-smart-testbed
-  authors: [Jingtian Yan, Zhifei Li, William Kang, Kevin Zheng, Yulun Zhang, Zhe Chen, Yue Zhang, Daniel Harabor, Stephen F. Smith, Jiaoyang Li]
-  venue: arXiv
-  year: 2025
-  thumbnail: /files/jiaoyangli/thumbnails/Yan25.png
-  eprint: arXiv:2503.04798
-  tags: [mapf, warehouse, execution]
-  links:
-    arXiv: https://arxiv.org/abs/2503.04798
-    Code: https://github.com/JingtianYan/SMART/
-    Demo: https://smart-mapf.github.io/demo/
-    Video: https://youtu.be/irtFxMjyJXs
-  abstract: "We present Scalable Multi-Agent Realistic Testbed (SMART), a realistic and efficient software tool for evaluating Multi-Agent Path Finding (MAPF) algorithms. MAPF focuses on planning collision-free paths for a group of agents. While state-of-the-art MAPF algorithms can plan paths for hundreds of robots in seconds, they often rely on simplified robot models, making their real-world performance unclear. Researchers typically lack access to hundreds of physical robots in laboratory settings to evaluate the algorithms. Meanwhile, industrial professionals who lack expertise in MAPF require an easy-to-use simulator to efficiently test and understand the performance of MAPF algorithms in their specific settings. SMART fills this gap with several advantages: (1) SMART uses a physics-engine-based simulator to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF algorithms and robot models, and (3) SMART scales to thousands of robots. In addition, we use SMART to explore and demonstrate research questions about the execution of MAPF algorithms in real-world scenarios. "

_data/pubs.yml

@@ -30,6 +30,61 @@
 #  abstract: null
 
 ############### 2026 ##################
+
+- key: Yan2026WinkTPG
+  title: "WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning"
+  site: https://jingtianyan.github.io/publication/2026-06-07-winktpg
+  authors: [Jingtian Yan, Stephen F. Smith, Jiaoyang Li]
+  venue: TASE
+  volume: 23
+  pages: 9162-9175
+  year: 2026
+  doi: 10.1109/TASE.2026.3688563
+  thumbnail: /files/jingtianyan/tase26_winktpg/winkTPG-Isaac-sim.gif
+  eprint: arXiv:2508.01495
+  tags: [mapf, warehouse, execution]
+  links:
+    arXiv: https://arxiv.org/abs/2508.01495
+    PDF: https://arxiv.org/pdf/2508.01495.pdf
+    Video: https://youtu.be/t9phIGjdiMk
+  abstract: "WinkTPG is an execution framework for Multi-Agent Path Finding (MAPF) that refines MAPF plans into kinodynamically feasible speed profiles through temporal reasoning. It combines kinodynamic Temporal Plan Graph planning with a windowed execution mechanism, allowing the system to incorporate updated agent information during execution and reduce timing uncertainty while preserving collision-avoidance guarantees."
+
+- key: Yan2026SMART
+  title: "Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)"
+  site: https://jingtianyan.github.io/smart-docs/
+  authors: [Jingtian Yan, Zhifei Li, William Kang, Kevin Zheng, Yulun Zhang, Zhe Chen, Yue Zhang, Daniel Harabor, Stephen F. Smith, Jiaoyang Li]
+  venue: RAL
+  volume: 11
+  number: 6
+  pages: 7428-7435
+  year: 2026
+  doi: 10.1109/LRA.2026.3688062
+  thumbnail: /files/jingtianyan/ral26_smart/SMART.gif
+  eprint: arXiv:2503.04798
+  tags: [mapf, warehouse, execution]
+  links:
+    arXiv: https://arxiv.org/abs/2503.04798
+    Code: https://github.com/JingtianYan/SMART/
+    Demo: https://smart-mapf.github.io/demo/
+    Video: https://youtu.be/irtFxMjyJXs
+  abstract: "We present Scalable Multi-Agent Realistic Testbed (SMART), a realistic and efficient software tool for evaluating Multi-Agent Path Finding (MAPF) algorithms. MAPF focuses on planning collision-free paths for a group of agents. While state-of-the-art MAPF algorithms can plan paths for hundreds of robots in seconds, they often rely on simplified robot models, making their real-world performance unclear. Researchers typically lack access to hundreds of physical robots in laboratory settings to evaluate the algorithms. Meanwhile, industrial professionals who lack expertise in MAPF require an easy-to-use simulator to efficiently test and understand the performance of MAPF algorithms in their specific settings. SMART fills this gap with several advantages: (1) SMART uses a physics-engine-based simulator to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF algorithms and robot models, and (3) SMART scales to thousands of robots. In addition, we use SMART to explore and demonstrate research questions about the execution of MAPF algorithms in real-world scenarios. "
+
+- key: YanICAPS26
+  title: "Analyzing Planner Design Trade-Offs for MAPF Under ADG-Based Realistic Execution"
+  site: https://jingtianyan.github.io/publication/2026-06-07-mapf-planner-tradeoffs
+  authors: [Jingtian Yan, Zhifei Li, William Kang, Stephen F. Smith, Jiaoyang Li]
+  venue: ICAPS
+  year: 2026
+  thumbnail: /files/jingtianyan/icaps26_design_tradeoff/icaps_design_smart.gif
+  tags: [mapf, warehouse, execution]
+  links:
+    arXiv: https://arxiv.org/abs/2512.09736
+    Code: null
+    Poster: null
+    Slides: null
+    Talk: null
+  abstract: "Multi-Agent Path Finding (MAPF) algorithms are increasingly deployed in industrial warehouses and automated manufacturing facilities, where robots must operate reliably under real-world physical constraints. However, existing MAPF evaluation frameworks typically rely on simplified robot models, leaving a substantial gap between algorithmic benchmarks and practical performance. Recent frameworks such as SMART combine kinodynamic modeling with execution based on the Action Dependency Graph (ADG), enabling realistic, large-scale MAPF evaluation. Building on this capability, this work investigates how key planner design choices influence performance under realistic execution settings. We systematically study three fundamental factors: (1) the relationship between solution optimality and execution performance, (2) the sensitivity of system performance to inaccuracies in kinodynamic modeling, and (3) the tradeoff between model accuracy and plan optimality. Empirically, we examine these factors to understand how these design choices affect performance in realistic scenarios. We highlight open challenges and research directions to steer the community toward practical, real-world deployment."
+
 - key: GouruICAPS26
   title: "Scalable Algorithms with Provable Optimality Bounds for the Multiple Watchman Route Problem"
   site: https://mwrp-cp3.github.io/  # project page
@@ -51,6 +106,7 @@
     Talk: null
   abstract: "In this paper, we tackle the Multiple Watchman Route Problem (MWRP), which aims to find a set of paths that M watchmen can follow such that every location on the map can be seen by at least one watchman. First, we propose multiple methods to reduce the state space over which a search needs to be conducted by pruning map areas that are guaranteed to be seen en route to other areas. Next, we introduce MWRP-CP3, an efficient optimal planner that combines these methods with techniques that improve the quality and calculation time of existing heuristics. We present several suboptimal algorithms with bounds on solution quality, including MxWA*, a general variant of weighted A* for makespan problems. We also present anytime variations of our suboptimal algorithms, as well as techniques to improve an existing suboptimal solution by solving multiple decomposed sub-problems. We show that MWRP-CP3 can reduce the search space by more than 95% and runs more than 200x faster than existing optimal algorithms on 2D grid maps. We also show that our suboptimal algorithms solve maps 3x larger than those solvable by MWRP-CP3. See https://mwrp-cp3.github.io/ for the open source codebase and video demonstrations."
 
+
 - key: VeerapaneniICRA26
   title: "Conflict-Based Search as a Protocol: A Multi-Agent Motion Planning Protocol for Heterogeneous Agents, Solvers, and Independent Tasks"
   site: https://rishi-v.github.io/CBS-Protocol/  # project page

_data/venues.yml

@@ -76,4 +76,7 @@ AutoRobots:
   type: journal
 RAL:
   name: "IEEE Robotics and Automation Letters"
-  type: journal
+  type: journal
+TASE:
+  name: "IEEE Transactions on Automation Science and Engineering"
+  type: journal

_publications/YanICAPS26.md

@@ -0,0 +1,16 @@
+---
+layout: publication
+permalink: /publications/YanICAPS26/
+author_profile: true
+---
+{% assign pub_key = "YanICAPS26" %}
+
+{% include base_path %}
+{% assign pub = null %}
+{% for p in site.data.pubs %}
+  {% if p.key == pub_key %}
+    {% assign pub = p %}
+    {% break %}
+  {% endif %}
+{% endfor %}
+{% include pub-page.html %}

beautiful-jekyll-theme.gemspec

@@ -22,6 +22,7 @@ Gem::Specification.new do |spec|
   spec.add_runtime_dependency "jekyll-sitemap", "~> 1.4"
   spec.add_runtime_dependency "kramdown-parser-gfm", "~> 1.1"
   spec.add_runtime_dependency "kramdown", "~> 2.3"
+  spec.add_runtime_dependency "logger"
   spec.add_runtime_dependency "webrick", "~> 1.8"
 
   spec.add_development_dependency "bundler", ">= 1.16"