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Large Language Models (LLMs) have shown promise as robotic planners but often struggle with long-horizon and complex tasks, especially in specialized environments requiring external knowledge.
Pddl—the planning domain definition language
McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., and Wilkins, D · 1998
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Val: Automatic plan validation, continuous effects and mixed initiative planning using pddl
Howey, R., Long, D., and Fox, M · 2004
Earlier work this paper cites.
A survey on policy search algorithms for learning robot controllers in a handful of trials
Chatzilygeroudis, K., Vassiliades, V., Stulp, F., Calinon, S., and Mouret, J.-B · 2019
Earlier work this paper cites.
Retrieval augmented language model pre-training
Guu, K., Lee, K., Tung, Z., Pasupat, P., and Chang, M · 2020
Earlier work this paper cites.
Hddl: An extension to pddl for expressing hierarchical planning problems
Höller, D., Behnke, G., Bercher, P., Biundo, S., Fiorino, H., Pellier, D., and Alford, R · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., et al · 2020
Earlier work this paper cites.
Translating natural language to planning goals with large-language models
Xie, Y · 2020
Earlier work this paper cites.
Keep calm and explore: Language models for action generation in text-based games
Yao, S., Rao, R., Hausknecht, M., and Narasimhan, K · 2020
Earlier work this paper cites.
Semantic-based explainable ai: Leveraging semantic scene graphs and pairwise ranking to explain robot failures
Das, D. and Chernova, S · 2021
Earlier work this paper cites.
Do as i can, not as i say: Grounding language in robotic affordances
Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al · 2022
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Roscoe: A suite of metrics for scoring step-by-step reasoning
Golovneva, O., Chen, M. P., Poff, S., Corredor, M., Zettlemoyer, L., Fazel-Zarandi, M., and Celikyilmaz, A · 2022
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Retrieval-augmented reinforcement learning
Goyal, A., Friesen, A., Banino, A., Weber, T., Ke, N. R., Badia, A. P., Guez, A., Mirza, M., Humphreys, P. C., Konyushova, K., et al · 2022
Earlier work this paper cites.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Huang, W., Abbeel, P., Pathak, D., and Mordatch, I · 2022
Earlier work this paper cites.
Hydra: A real-time spatial perception system for 3D scene graph construction and optimization
Hughes, N., Chang, Y., and Carlone, L · 2022
Earlier work this paper cites.
Pre-trained language models for interactive decision-making
Li, S., Puig, X., Paxton, C., Du, Y., Wang, C., Fan, L., Chen, T., Huang, D.-A., Akyürek, E., Anandkumar, A., et al · 2022
Earlier work this paper cites.
Plansformer: Generating symbolic plans using transformers
Pallagani, V., Muppasani, B., Murugesan, K., Rossi, F., Horesh, L., Srivastava, B., Fabiano, F., and Loreggia, A · 2022
Earlier work this paper cites.
Planning with large language models via corrective re-prompting
Raman, S. S., Cohen, V., Rosen, E., Idrees, I., Paulius, D., and Tellex, S · 2022
Earlier work this paper cites.
Pddl planning with pretrained large language models
Silver, T., Hariprasad, V., Shuttleworth, R. S., Kumar, N., Lozano-Pérez, T., and Kaelbling, L. P · 2022
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E. H., Le, Q., and Zhou, D · 2022
Cited alongside, same era.
Gemini: A family of highly capable multimodal models
Anil, R., Borgeaud, S., Wu, Y., Alayrac, J., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., Silver, D., Petrov, S., Johnson, M., Antonoglou, I., Schrittwieser, J., Glaese, A., Chen, J., Pitler, E., Lillicrap, T. P., Lazaridou, A., Firat, O., Molloy, J., Isard, M., Barham, P. R., Hennigan, T., Lee, B., Viola, F., Reynolds, M., Xu, Y., Doherty, R., Collins, E., Meyer, C., Rutherford, E., Moreira, E., Ayoub, K., Goel, M., Tucker, G., Piqueras, E., Krikun, M., Barr, I., Savinov, N., Danihelka, I., Roelofs, B., White, A., Andreassen, A., von Glehn, T., Yagati, L., Kazemi, M., Gonzalez, L., Khalman, M., Sygnowski, J., and et al · 2023
Cited alongside, same era.
Grounding large language models in interactive environments with online reinforcement learning
Carta, T., Romac, C., Wolf, T., Lamprier, S., Sigaud, O., and Oudeyer, P.-Y · 2023
Translating natural language to planning goals with large-language models
Xie, Y., Yu, C., Zhu, T., Bai, J., Gong, Z., and Soh, H · 2023
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Phi-3 technical report: A highly capable language model locally on your phone
Abdin, M. I., Jacobs, S. A., Awan, A. A., Aneja, J., Awadallah, A., Awadalla, H., Bach, N., Bahree, A., Bakhtiari, A., Behl, H. S., Benhaim, A., Bilenko, M., Bjorck, J., Bubeck, S., Cai, M., Mendes, C. C. T., Chen, W., Chaudhary, V., Chopra, P., Giorno, A. D., de Rosa, G., Dixon, M., Eldan, R., Iter, D., Garg, A., Goswami, A., Gunasekar, S., Haider, E., Hao, J., Hewett, R. J., Huynh, J., Javaheripi, M., Jin, X., Kauffmann, P., Karampatziakis, N., Kim, D., Khademi, M., Kurilenko, L., Lee, J. R., Lee, Y. T., Li, Y., Liang, C., Liu, W., Lin, E., Lin, Z., Madan, P., Mitra, A., Modi, H., Nguyen, A., Norick, B., Patra, B., Perez-Becker, D., Portet, T., Pryzant, R., Qin, H., Radmilac, M., Rosset, C., Roy, S., Ruwase, O., Saarikivi, O., Saied, A., Salim, A., Santacroce, M., Shah, S., Shang, N., Sharma, H., Song, X., Tanaka, M., Wang, X., Ward, R., Wang, G., Witte, P., Wyatt, M., Xu, C., Xu, J., Yadav, S., Yang, F., Yang, Z., Yu, D., Zhang, C., Zhang, C., Zhang, J., Zhang, L. L., Zhang, Y., Zhang, Y., Zhang, Y., and Zhou, X · 2024
Later among the works it cites.
Compositional foundation models for hierarchical planning
Ajay, A., Han, S., Du, Y., Li, S., Gupta, A., Jaakkola, T., Tenenbaum, J., Kaelbling, L., Srivastava, A., and Agrawal, P · 2024
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Recover: A neuro-symbolic framework for failure detection and recovery
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Cited alongside, same era.
Leveraging pre-trained large language models to construct and utilize world models for model-based task planning
Guan, L., Valmeekam, K., Sreedharan, S., and Kambhampati, S · 2023
Cited alongside, same era.
Code as policies: Language model programs for embodied control
Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., and Zeng, A · 2023
Cited alongside, same era.
Neuro-symbolic procedural planning with commonsense prompting
Lu, Y., Feng, W., Zhu, W., Xu, W., Wang, X. E., Eckstein, M., and Wang, W. Y · 2023
Cited alongside, same era.
Sayplan: Grounding large language models using 3d scene graphs for scalable task planning
Rana, K., Haviland, J., Garg, S., Abou-Chakra, J., Reid, I. D., and Suenderhauf, N · 2023
Cited alongside, same era.
Robots that ask for help: Uncertainty alignment for large language model planners
Ren, A. Z., Dixit, A., Bodrova, A., Singh, S., Tu, S., Brown, N., Xu, P., Takayama, L., Xia, F., Varley, J., et al · 2023
Cited alongside, same era.
Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessi, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
Cited alongside, same era.
Progprompt: Generating situated robot task plans using large language models
Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., and Garg, A · 2023
Cited alongside, same era.
Skreta, M., Yoshikawa, N., Arellano-Rubach, S., Ji, Z., Kristensen, L. B., Darvish, K., Aspuru-Guzik, A., Shkurti, F., and Garg, A · 2023
Cited alongside, same era.
Cornelio, C. and Diab, M · 2024
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From local to global: A graph rag approach to query-focused summarization
Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., and Larson, J · 2024
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Rag-modulo: Solving sequential tasks using experience, critics, and language models
Jain, A., Jermaine, C., and Unhelkar, V · 2024
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Robotgpt: Robot manipulation learning from chatgpt
Jin, Y., Li, D., Yong, A., Shi, J., Hao, P., Sun, F., Zhang, J., and Fang, B · 2024
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Rap: Retrieval-augmented planning with contextual memory for multimodal llm agents
Kagaya, T., Yuan, T. J., Lou, Y., Karlekar, J., Pranata, S., Kinose, A., Oguri, K., Wick, F., and You, Y · 2024
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Rada: Retrieval-augmented web agent planning with llms
Kim, M., Bursztyn, V., Koh, E., Guo, S., and Hwang, S.-w · 2024
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Unifying large language models and knowledge graphs: A roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., and Wu, X · 2024
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Saynav: Grounding large language models for dynamic planning to navigation in new environments
Rajvanshi, A., Sikka, K., Lin, X., Lee, B., Chiu, H.-P., and Velasquez, A · 2024
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Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J., Ellenberg, J. S., Wang, P., Fawzi, O., et al · 2024
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Solving olympiad geometry without human demonstrations
Trinh, T. H., Wu, Y., Le, Q. V., He, H., and Luong, T · 2024
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Wu, Y., Zhang, J., Hu, N., Tang, L., Qi, G., Shao, J., Ren, J., and Song, W · 2024
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P-rag: Progressive retrieval augmented generation for planning on embodied everyday task
Xu, W., Wang, M., Zhou, W., and Li, H · 2024
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Larger and more instructable language models become less reliable
Zhou, L., Schellaert, W., Martínez-Plumed, F., Moros-Daval, Y., Ferri, C., and Hernández-Orallo, J · 2024
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