Update README.md

README.md

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-Edit this `README.md` markdown file to author your organization card.
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+# LLM_4_robotics
+
+##Overview
+
+LLM-4-Robotics is an organization dedicated to exploring the integration of Large Language Models (LLMs) in robotics applications. Our aim is to leverage the power of advanced natural language processing techniques to enhance various aspects of robotics, including perception, reasoning, planning, and navigation.
+
+
+##Purpose
+
+The purpose of LLM-4-Robotics is to:
+
+* Investigate and develop innovative approaches for incorporating LLMs into robotics systems.
+* Explore the potential of LLMs in enhancing robot perception, understanding of human language and intent, and decision-making capabilities.
+* Bridge the gap between natural language understanding and robotic actions, enabling more intuitive human-robot interaction.
+* Contribute to the advancement of robotics research by leveraging state-of-the-art language models.
+
+
+##Works and Publications
+
+____________________________________________________________________________________________________________________________________________
+
+### Survey Papers
+* "Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis", *arXiv, Dec 2023*. [[Paper](https://arxiv.org/abs/2312.08782)] [[Paper List](https://github.com/JeffreyYH/robotics-fm-survey)] [[Website](https://robotics-fm-survey.github.io/)] 
+* "Language-conditioned Learning for Robotic Manipulation: A Survey", *arXiv, Dec 2023*, [[Paper](https://arxiv.org/abs/2312.10807)] 
+* "Foundation Models in Robotics: Applications, Challenges, and the Future", *arXiv, Dec 2023*, [[Paper](https://arxiv.org/abs/2312.07843)] [[Paper List](https://github.com/robotics-survey/Awesome-Robotics-Foundation-Models)]
+* "Robot Learning in the Era of Foundation Models: A Survey", *arXiv, Nov 2023*, [[Paper](https://arxiv.org/abs/2311.14379)]
+* "The Development of LLMs for Embodied Navigation", *arXiv, Nov 2023*, [[Paper](https://arxiv.org/abs/2311.00530)]
+____________________________________________________________________________________________________________________________________________
+
+## Planning
+* **BTGenBot**: "BTGenBot: Behavior Tree Generation for Robotic Tasks with Lightweight LLMs", *arXiv, March 2024*. [[Paper](https://arxiv.org/abs/2403.12761)][[Github](https://github.com/AIRLab-POLIMI/BTGenBot)]
+* **Attentive Support**: "To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions", *arXiv, March 2024*. [[Paper](https://arxiv.org/abs/2403.12533)] [[Website](https://hri-eu.github.io/AttentiveSupport/)][[Code](https://github.com/HRI-EU/AttentiveSupport)]
+* **SayCanPay**: "SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge", AAAI Jan 2024, [[Paper](https://arxiv.org/abs/2308.12682)] [[Code](https://github.com/RishiHazra/saycanpay)] [[Website](https://rishihazra.github.io/SayCanPay/)]
+* **ViLa**: "Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning", *arXiv, Sep 2023*, [[Paper](https://arxiv.org/abs/2311.17842)] [[Website](https://robot-vila.github.io/)]
+* **-**: "When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applications", *arXiv, Oct 2023*. [[Paper](https://arxiv.org/abs/2310.07263)]
+* **CoPAL**: "Corrective Planning of Robot Actions with Large Language Models", *ICRA, Oct 2023*. [[Paper](https://arxiv.org/abs/2310.07263)] [[Website](https://hri-eu.github.io/Loom/)][[Code](https://github.com/HRI-EU/Loom/tree/main)]
+* **LGMCTS**: "LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement", *arXiv, Sep 2023*. [[Paper](https://arxiv.org/abs/2309.15821)]
+* **Prompt2Walk**: "Prompt a Robot to Walk with Large Language Models", *arXiv, Sep 2023*, [[Paper](https://arxiv.org/abs/2309.09969)] [[Website](https://prompt2walk.github.io)]
+* **DoReMi**: "Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment", *arXiv, July 2023*, [[Paper](https://arxiv.org/abs/2307.00329)] [[Website](https://sites.google.com/view/doremi-paper)]
+* **Co-LLM-Agents**: "Building Cooperative Embodied Agents Modularly with Large Language Models", *arXiv, Jul 2023*. [[Paper](https://arxiv.org/abs/2307.02485)] [[Code](https://github.com/UMass-Foundation-Model/Co-LLM-Agents)] [[Website](https://vis-www.cs.umass.edu/Co-LLM-Agents/)]
+* **LLM-Reward**: "Language to Rewards for Robotic Skill Synthesis", *arXiv, Jun 2023*. [[Paper](https://arxiv.org/abs/2306.08647)] [[Website](https://language-to-reward.github.io/)]
+* **LLM-BRAIn**: "LLM-BRAIn: AI-driven Fast Generation of Robot Behaviour Tree based on Large Language Model", *arXiv, May 2023*. [[Paper](https://arxiv.org/abs/2305.19352)]
+* **GLAM**: "Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning", *arXiv, May 2023*. [[Paper](https://arxiv.org/abs/2302.02662)] [[Pytorch Code](https://github.com/flowersteam/Grounding_LLMs_with_online_RL)] 
+* **LLM-MCTS**: "Large Language Models as Commonsense Knowledge for Large-Scale Task Planning", *arXiv, May 2023*. [[Paper](https://arxiv.org/abs/2305.14078v1)] 
+* **AlphaBlock**: "AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation", *arxiv, May 2023*. [[Paper](https://arxiv.org/abs/2305.18898)]
+* **LLM+P**:"LLM+P: Empowering Large Language Models with Optimal Planning Proficiency", *arXiv, Apr 2023*, [[Paper](https://arxiv.org/abs/2304.11477)] [[Code](https://github.com/Cranial-XIX/llm-pddl)]
+* **ReAct**: "ReAct: Synergizing Reasoning and Acting in Language Models", *ICLR, Apr 2023*. [[Paper](https://arxiv.org/abs/2210.03629)] [[Github](https://github.com/ysymyth/ReAct)] [[Website](https://react-lm.github.io/)]
+* **LLM-planner**: "LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models", *arXiv, Mar 2023*. [[Paper](https://arxiv.org/abs/2212.04088)] [[Pytorch Code](https://github.com/OSU-NLP-Group/LLM-Planner/)] [[Website](https://dki-lab.github.io/LLM-Planner/)]
+* **Text2Motion**: "Text2Motion: From Natural Language Instructions to Feasible Plans", *arXiV, Mar 2023*, [[Paper](https://arxiv.org/abs/2303.12153)] [[Website](https://sites.google.com/stanford.edu/text2motion)]
+* "Reward Design with Language Models", *ICML, Feb 2023*. [[Paper](https://arxiv.org/abs/2303.00001v1)] [[Pytorch Code](https://github.com/minaek/reward_design_with_llms)] 
+* **Don't Copy the Teacher**: "Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue", *EMNLP, Oct 2022*. [[Paper](https://arxiv.org/abs/2210.04443)] [[Website](https://www.youtube.com/watch?v=qGPC65BDJw4&t=2s)]
+* **FILM**: "FILM: Following Instructions in Language with Modular Methods", *ICLR, Apr 2022*. [[Paper](https://arxiv.org/abs/2110.07342)] [[Code](https://github.com/soyeonm/FILM)] [[Website](https://soyeonm.github.io/FILM_webpage/)]
+* **LID**: "Pre-Trained Language Models for Interactive Decision-Making", *arXiv, Feb 2022*. [[Paper](https://arxiv.org/abs/2202.01771)] [[Pytorch Code](https://github.com/ShuangLI59/Language-Model-Pre-training-Improves-Generalization-in-Policy-Learning)] [[Website](https://shuangli-project.github.io/Pre-Trained-Language-Models-for-Interactive-Decision-Making/)]
+* "Collaborating with language models for embodied reasoning", *NeurIPS, Feb 2022*. [[Paper](https://arxiv.org/abs/2302.00763v1)]
+* **ZSP**: "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents", *ICML, Jan 2022*. [[Paper](https://arxiv.org/abs/2201.07207)] [[Pytorch Code](https://github.com/huangwl18/language-planner)] [[Website](https://wenlong.page/language-planner/)]
+* "Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions", *arXiV, Oct 2020*, [[Paper](https://arxiv.org/abs/2009.14259)] 
+
+____________________________________________________________________________________________________________________________________________