teaser and template.yaml updates

template.yaml

@@ -1,60 +1,53 @@
 theme: default # default || dark
 organization: OMRON SINIC X
 twitter: '@omron_sinicx'
-title: 'Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Manipulation by Mobile Robots'
+title: 'Language-Guided Pattern Formation for Swarm Robotics with Multi-Agent Reinforcement Learning'
 conference: IROS2024
 resources:
-  paper: https://arxiv.org/abs/1909.13111
-  code: https://github.com/omron-sinicx/multipolar
-  video: https://www.youtube.com/embed/W8nBFKDxsb0
-  blog: https://medium.com/sinicx/multipolar-multi-source-policy-aggregation-for-transfer-reinforcement-learning-between-diverse-bc42a152b0f5
-  huggingface: https://huggingface.co/
-description: explore a new challenge in transfer RL, where only a set of source policies collected under unknown diverse dynamics is available for learning a target task efficiently.
-image: https://omron-sinicx.github.io/multipolar/assets/teaser.png
-url: https://omron-sinicx.github.io/multipolar
-speakerdeck: b7a0614c24014dcbbb121fbb9ed234cd
+  paper: null
+  code: null
+  video: https://www.youtube.com/embed/fCVIwcC6oM4
+  blog: null
+  huggingface: null
+description: This paper explores how to leverage the vast knowledge encoded in large language models to tackle pattern formation challenges for swarm robotics systems.
+image: https://omron-sinicx.github.io/language-guided-pattern-formation/assets/teaser.png
+url: https://omron-sinicx.github.io/language-guided-pattern-formation/
+speakerdeck: null
 authors:
-  - name: So Kuroki
-    affiliation: [1, 2]
-    url: http://barekatain.me/
-    position: intern
-  - name: Mai Nishimura
+  - name: Hsu-Shen Liu*
     affiliation: [1]
-    position: Senior Researcher
-    url: https://denkiwakame.github.io
+    url: null
+    position: intern
+  - name: So Kuroki*
+    affiliation: [2]
+    position: intern
+    url: null
   - name: Tadashi Kozuno
-    affiliation: [1]
+    affiliation: [3]
     position: Senior Researcher
-    url: https://sites.google.com/view/masashihamaya/home
-    #  - name: Mai Nishimura
-    #    affiliation: [1]
-    #    url: https://denkiwakame.github.io
-    #  - name: Asako Kanezaki
-    #    affiliation: [2]
-    #    url: https://kanezaki.github.io/
-contact_ids: ['github', 'omron', 2] #=> github issues, [email protected], 2nd author
+    url: https://tadashik.github.io/
+  - name: Wei-Fang Sun
+    affiliation: [4]
+    position: Researcher
+    url: https://home.j3soon.com/
+  - name: Chun-Yi Lee
+    affiliation: [1]
+    position: Professor
+    url: https://elsalab.ai/
+contact_ids: ['github', 'omron', 3] #=> github issues, [email protected], 2nd author
 affiliations:
-  - OMRON SINIC X Corporation
-  - Technical University of Munich
+  - ELSA Lab, National Tsing Hua University
+  - The University of Tokyo
+  - OMRON SINIC X
+  - NVIDIA Research
 meta:
-  - '* work done as an intern at OMRON SINIC X.'
+  - '* work done as interns at OMRON SINIC X.'
 bibtex: >
-  # arXiv version
-
-  @article{barekatain2019multipolar,
-    title={MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics},
-    author={Barekatain, Mohammadamin and Yonetani, Ryo and Hamaya, Masashi},
-    journal={arXiv preprint arXiv:1909.13111},
-    year={2019}
-  }
-
-  # IJCAI version
-
-  @inproceedings{barekatain2020multipolar,
-    title={MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics},
-    author={Barekatain, Mohammadamin and Yonetani, Ryo and Hamaya, Masashi},
-    booktitle={International Joint Conference on Artificial Intelligence (IJCAI)},
-    year={2020}
+  @inproceedings{liu2024language,
+    title={Language-Guided Pattern Formation for Swarm Robotics with Multi-Agent Reinforcement Learning},
+    author={Liu, Hsu-Shen and Kuroki, So and Kozuno, Tadashi and Sun, Wei-Fang and Lee, Chun-Yi},
+    booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
+    year={2024}
   }
 header:
   bg_curve:
@@ -63,9 +56,7 @@ header:
 
 teaser: teaser.png
 overview: |
-  This is a versatile template designed to satisfy your research project page needs, all while harnessing the power of **UIKit** and **React**. Built on the foundations of simplicity and flexibility, this template allows you to focus on expressing your ideas without the hassle of directly handling CSS—thanks to customizable SASS variables.
-  With markdown as your canvas and $\KaTeX$ for precise equations, crafting clear and engaging project page becomes effortless. Whether you're unraveling complex theories or presenting your findings, this template aims to support your scholarly endeavors with grace and ease.
-  *Need to edit HTML directly?* Fear not! In addition to markdown, you can also directly write HTML with ease. Feel empowered to craft your content exactly as you envision it, whether through markdown's simplicity or the precision of HTML.
+  This paper explores leveraging the vast knowledge encoded in Large Language Models (LLMs) to tackle pattern formation challenges for swarm robotics systems. A new framework, named LGPF (Language-Guided Pattern Formation), is proposed to address these challenges. The framework breaks down the pattern formation into two key components: pattern synthesis and swarm robotics control. For the former, this study utilizes the exceptional few-shot generalizability of LLMs to translate high-level natural language descriptions into the desired spatial pattern coordinates. This approach allows for overcoming previous limitations in representing and designing complex patterns. The framework further employs a centralized training with decentralized execution (CTDE) based multi-agent reinforcement learning (MARL) approach to control the swarm robots in forming the specified pattern while avoiding collisions. The decentralized policies learned with the CTDE-based MARL algorithm consider coordination between robots without direct communication under a partially observable setup. To validate the effectiveness of our framework, we perform extensive experiments in both simulation and real-world environments. These experiments validate LGPF's effectiveness in accurately and safely forming diverse user-specified patterns.
 
 body:
   - title: Media examples
@@ -233,9 +224,9 @@ projects:
     description: |
       "maru" (= miniature assemblage adaptive robot unit) is a custom-made, miniature-sized, two-wheeled robot designed specifically for tabletop swarm robotics research.
     url: https://github.com/omron-sinicx/swarm-body
-  - title: 'Language-Guided Pattern Formation for Swarm Robotics with Multi-Agent Reinforcement Learning'
+  - title: 'Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Push Manipulation by Mobile Robots'
     journal: "IROS'24"
-    img: 'https://github.com/omron-sinicx/swarm-body/raw/main/images/teaser.jpg'
+    img: 'https://github.com/omron-sinicx/mabr/raw/project-page/src/media/teaser.png'
     description: |
-      "maru" (= miniature assemblage adaptive robot unit) is a custom-made, miniature-sized, two-wheeled robot designed specifically for tabletop swarm robotics research.
+      The multi-agent coordination skill database allows multiple mobile robots to efficiently use past memories to adapt to new tasks.
     url: https://github.com/omron-sinicx/swarm-body