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Existing methods for vision-language task planning excel in short-horizon tasks but often fall short in complex, long-horizon planning within dynamic environments.
Hierarchical foresight: Self-supervised learning of long-horizon tasks via visual subgoal generation
Suraj Nair and Chelsea Finn. 2019 · 1909
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Ff: The fast-forward planning system
Jörg Hoffmann. 2001 · 2001
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The fast downward planning system
Malte Helmert. 2006 · 2006
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A tutorial on planning graph based reachability heuristics
Daniel Bryce and Subbarao Kambhampati. 2007 · 2007
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A heuristic search approach to planning with temporally extended preferences
Jorge A Baier, Fahiem Bacchus, and Sheila A McIlraith. 2009 · 2009
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Automatic analysis of syntactic complexity in second language writing
Xiaofei Lu. 2010 · 2010
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Alfworld: Aligning text and embodied environments for interactive learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. 2020b · 2010
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Task planning in robotics: an empirical comparison of pddl-based and asp-based systems
Yuqian Jiang, Shiqi Zhang, Piyush Khandelwal, and Peter Stone. 2018 · 2018
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Learning plannable representations with causal infogan
Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J Russell, and Pieter Abbeel. 2018 · 2018
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Virtualhome: Simulating household activities via programs
Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba. 2018 · 2018
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Universal planning networks: Learning generalizable representations for visuomotor control
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn. 2018 · 2018
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Neural task programming: Learning to generalize across hierarchical tasks
Danfei Xu, Suraj Nair, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, and Silvio Savarese. 2018 · 2018
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Search on the replay buffer: Bridging planning and reinforcement learning
Ben Eysenbach, Russ R Salakhutdinov, and Sergey Levine. 2019 · 2019
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Language as an abstraction for hierarchical deep reinforcement learning
Yiding Jiang, Shixiang Shane Gu, Kevin P Murphy, and Chelsea Finn. 2019 · 2019
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Synthesizing environment-aware activities via activity sketches
Yuan-Hong Liao, Xavier Puig, Marko Boben, Antonio Torralba, and Sanja Fidler. 2019 · 2019
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Regression planning networks
Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, and Li F Fei-Fei. 2019 · 2019
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Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning
Caelan Reed Garrett, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. 2020 · 2020
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Habitat 2.0: Training home assistants to rearrange their habitat
Andrew Szot, Alexander Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Singh Chaplot, Oleksandr Maksymets, et al. 2021 · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. 2022 · 2022
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Pre-trained language models for interactive decision-making
Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, et al. 2022 · 2022
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Pddl planning with pretrained large language models
Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. 2022 · 2022
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Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022 · 2022
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Openflamingo: An open-source framework for training large autoregressive vision-language models
Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, et al. 2023 · 2023
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Do as i can, not as i say: Grounding language in robotic affordances
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al. 2023 · 2023
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Llm-state: Expandable state representation for long-horizon task planning in the open world
Siwei Chen, Anxing Xiao, and David Hsu. 2023 · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al. 2023 · 2023
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Large language models as zero-shot human models for human-robot interaction
Bowen Zhang and Harold Soh. 2023 · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
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Pravesh Agrawal, Szymon Antoniak, Emma Bou Hanna, Devendra Chaplot, Jessica Chudnovsky, Saurabh Garg, Theophile Gervet, Soham Ghosh, Amélie Héliou, Paul Jacob, et al. 2024 · 2024
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Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
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Palm-e: An embodied multimodal language model
Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. 2023 · 2023
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Self-alignment with instruction backtranslation
Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Omer Levy, Luke Zettlemoyer, Jason Weston, and Mike Lewis. 2023 · 2023
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Code as policies: Language model programs for embodied control
Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng. 2023 · 2023
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OpenAI. 2023 · 2023
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Embodied executable policy learning with language-based scene summarization
Jielin Qiu, Mengdi Xu, William Han, Seungwhan Moon, and Ding Zhao. 2023 · 2023
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Sayplan: Grounding large language models using 3d scene graphs for scalable task planning
Krishan Rana, Jesse Haviland, Sourav Garg, Jad Abou-Chakra, Ian Reid, and Niko Suenderhauf. 2023 · 2023
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Generalized planning in pddl domains with pretrained large language models
Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B Tenenbaum, Leslie Pack Kaelbling, and Michael Katz. 2023 · 2023
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Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, and Farshad Khorrami. 2024 · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
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Glore: When, where, and how to improve llm reasoning via global and local refinements
Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, Jane Dwivedi-Yu, Maksym Zhuravinskyi, Eric Hambro, and Roberta Raileanu. 2024 · 2024
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Bootstrapping vision-language learning with decoupled language pre-training
Yiren Jian, Chongyang Gao, and Soroush Vosoughi. 2024 · 2024
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Modality-fair preference optimization for trustworthy mllm alignment
Songtao Jiang, Yan Zhang, Ruizhe Chen, Yeying Jin, and Zuozhu Liu. 2024 · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024 · 2024
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Hello gpt-4o
OpenAI. 2024 · 2024
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Iterative reasoning preference optimization
Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He, Sainbayar Sukhbaatar, and Jason Weston. 2024 · 2024
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Self-consistency preference optimization
Archiki Prasad, Weizhe Yuan, Richard Yuanzhe Pang, Jing Xu, Maryam Fazel-Zarandi, Mohit Bansal, Sainbayar Sukhbaatar, Jason Weston, and Jane Yu. 2024 · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
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Direct nash optimization: Teaching language models to self-improve with general preferences
Corby Rosset, Ching-An Cheng, Arindam Mitra, Michael Santacroce, Ahmed Awadallah, and Tengyang Xie. 2024 · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. 2024 · 2024
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Self-rewarding language models
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. 2024 · 2024
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Chain of preference optimization: Improving chain-of-thought reasoning in llms
Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, and Min Lin. 2024 · 2024
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Large language models as commonsense knowledge for large-scale task planning
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2024 · 2024
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Pragmatic instruction following and goal assistance via cooperative language-guided inverse planning
Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka, and Joshua B Tenenbaum. 2024 · 2024
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Qwen2.5-vl
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Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models
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