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Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences.
Constrained Markov decision processes , volume 7
Eitan Altman · 1999
Earlier work this paper cites.
Automated Planning: theory and practice
Malik Ghallab, Dana Nau, and Paolo Traverso · 2004
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
Earlier work this paper cites.
Leveraging human guidance for deep reinforcement learning tasks
Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H Ballard, and Peter Stone · 2019
Earlier work this paper cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
Earlier work this paper cites.
Polanyi’s revenge and ai’s new romance with tacit knowledge
Subbarao Kambhampati · 2021
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The surprising effectiveness of representation learning for visual imitation
Jyothish Pari, Nur Muhammad Shafiullah, Sridhar Pandian Arunachalam, and Lerrel Pinto · 2021
Earlier work this paper cites.
Visual imitation made easy
Sarah Young, Dhiraj Gandhi, Shubham Tulsiani, Abhinav Gupta, Pieter Abbeel, and Lerrel Pinto · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
Earlier work this paper cites.
Can foundation models perform zero-shot task specification for robot manipulation?
Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, and Aravind Rajeswaran · 2022
Earlier work this paper cites.
Vip: Towards universal visual reward and representation via value-implicit pre-training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang · 2022
Earlier work this paper cites.
R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2022
Earlier work this paper cites.
Talm: Tool augmented language models
Aaron Parisi, Yao Zhao, and Noah Fiedel · 2022
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Correcting robot plans with natural language feedback
Pratyusha Sharma, Balakumar Sundaralingam, Valts Blukis, Chris Paxton, Tucker Hermans, Antonio Torralba, Jacob Andreas, and Dieter Fox · 2022
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Socratic models: Composing zero-shot multimodal reasoning with language
Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, et al · 2022
Earlier work this paper cites.
Dino: Detr with improved denoising anchor boxes for end-to-end object detection
Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung-Yeung Shum · 2022
Earlier work this paper cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Earlier work this paper cites.
Latte: Language trajectory transformer
Arthur Bucker, Luis Figueredo, Sami Haddadin, Ashish Kapoor, Shuang Ma, Sai Vemprala, and Rogerio Bonatti · 2023
Earlier work this paper cites.
No, to the right: Online language corrections for robotic manipulation via shared autonomy
Yuchen Cui, Siddharth Karamcheti, Raj Palleti, Nidhya Shivakumar, Percy Liang, and Dorsa Sadigh · 2023
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Aligndiff: Aligning diverse human preferences via behavior-customisable diffusion model
Zibin Dong, Yifu Yuan, Jianye Hao, Fei Ni, Yao Mu, Yan Zheng, Yujing Hu, Tangjie Lv, Changjie Fan, and Zhipeng Hu · 2023
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Foundation models in robotics: Applications, challenges, and the future
Roya Firoozi, Johnathan Tucker, Stephen Tian, Anirudha Majumdar, Jiankai Sun, Weiyu Liu, Yuke Zhu, Shuran Song, Ashish Kapoor, Karol Hausman, et al · 2023
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Leveraging pre-trained large language models to construct and utilize world models for model-based task planning
Lin Guan, Karthik Valmeekam, Sarath Sreedharan, and Subbarao Kambhampati · 2023
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Contrastive prefence learning: Learning from human feedback without rl
Shepherd: A critic for language model generation
Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan, Sean O’Brien, Ramakanth Pasunuru, Jane Dwivedi-Yu, Olga Golovneva, Luke Zettlemoyer, Maryam Fazel-Zarandi, and Asli Celikyilmaz · 2023
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Learning adaptive planning representations with natural language guidance
Lionel Wong, Jiayuan Mao, Pratyusha Sharma, Zachary S Siegel, Jiahai Feng, Noa Korneev, Joshua B Tenenbaum, and Jacob Andreas · 2023
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Visual chatgpt: Talking, drawing and editing with visual foundation models
Chenfei Wu, Shengming Yin, Weizhen Qi, Xiaodong Wang, Zecheng Tang, and Nan Duan · 2023
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Text2reward: Automated dense reward function generation for reinforcement learning
Tianbao Xie, Siheng Zhao, Chen Henry Wu, Yitao Liu, Qian Luo, Victor Zhong, Yanchao Yang, and Tao Yu · 2023
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Joey Hejna, Rafael Rafailov, Harshit Sikchi, Chelsea Finn, Scott Niekum, W Bradley Knox, and Dorsa Sadigh · 2023
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Large language models cannot self-correct reasoning yet
Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou · 2023
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Rlaif: Scaling reinforcement learning from human feedback with ai feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi · 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
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Reflect: Summarizing robot experiences for failure explanation and correction
Zeyi Liu, Arpit Bahety, and Shuran Song · 2023
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Eureka: Human-level reward design via coding large language models
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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Chatmpc: Natural language based mpc personalization
Yuya Miyaoka, Masaki Inoue, and Tomotaka Nii · 2023
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Mengjiao Yang, Yilun Du, Kamyar Ghasemipour, Jonathan Tompson, Dale Schuurmans, and Pieter Abbeel · 2023
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Language to rewards for robotic skill synthesis
Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, et al · 2023
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Large language models for robotics: A survey
Fanlong Zeng, Wensheng Gan, Yongheng Wang, Ning Liu, and Philip S Yu · 2023
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Distilling and retrieving generalizable knowledge for robot manipulation via language corrections
Lihan Zha, Yuchen Cui, Li-Heng Lin, Minae Kwon, Montserrat Gonzalez Arenas, Andy Zeng, Fei Xia, and Dorsa Sadigh · 2023
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Learning fine-grained bimanual manipulation with low-cost hardware
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn · 2023
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et al · 2023
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Autort: Embodied foundation models for large scale orchestration of robotic agents
Michael Ahn, Debidatta Dwibedi, Chelsea Finn, Montse Gonzalez Arenas, Keerthana Gopalakrishnan, Karol Hausman, Brian Ichter, Alex Irpan, Nikhil Joshi, Ryan Julian, et al · 2024
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Spatialvlm: Endowing vision-language models with spatial reasoning capabilities
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Video language planning
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Position: Llms can’t plan, but can help planning in llm-modulo frameworks
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Motif: Intrinsic motivation from artificial intelligence feedback
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On the self-verification limitations of large language models on reasoning and planning tasks
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Solving olympiad geometry without human demonstrations
Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong · 2024
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J Yow, Neha Priyadarshini Garg, Manoj Ramanathan, Wei Tech Ang, et al · 2024
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Self-rewarding language models
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