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The state of an object reflects its current status or condition and is important for a robot's task planning and manipulation.
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Kalashnikov, D., Irpan, A., Pastor, P., Ibarz, J., Herzog, A., Jang, E., et al.: Scalable deep reinforcement learning for vision-based robotic manipulation. In: Conference on Robot Learning (CoRL). pp. 651–673. PMLR (2018)
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Nyga, D., Roy, S., Paul, R., Park, D., Pomarlan, M., Beetz, M., Roy, N.: Grounding robot plans from natural language instructions with incomplete world knowledge. In: Conference on Robot Learning (CoRL). pp. 714–723. PMLR (2018)
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Beik Mohammadi, H., Zamani, M.A., Kerzel, M., Wermter, S.: Mixed-reality deep reinforcement learning for a reach-to-grasp task. In: International Conference on Artificial Neural Networks. pp. 611–623. Springer (2019)
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. pp. 8748–8763. PMLR (2021)
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Chen, A., Sharma, A., Levine, S., Finn, C.: You only live once: Single-life reinforcement learning. In: Advances in Neural Information Processing Systems. vol. 35, pp. 14784–14797 (2022)
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Gouidis, F., Patkos, T., Argyros, A., Plexousakis, D.: Detecting object states vs detecting objects: A new dataset and a quantitative experimental study. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. vol. 5, pp. 590–600 (2022)
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Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. In: International Conference on Learning Representations (2022)
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Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., et al.: Inner Monologue: Embodied reasoning through planning with language models. In: Conference on Robot Learning (CoRL). vol. 205, pp. 1769–1782. PMLR (2022)
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Jang, E., Irpan, A., Khansari, M., Kappler, D., Ebert, F., Lynch, C., Levine, S., Finn, C.: BC-Z: Zero-shot task generalization with robotic imitation learning. In: Conference on Robot Learning (CoRL). pp. 991–1002. PMLR (2022)
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 10684–10695 (2022)
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Sun, X., Weber, C., Kerzel, M., Weber, T., Li, M., Wermter, S.: Learning visually grounded human-robot dialog in a hybrid neural architecture. In: International Conference on Artificial Neural Networks. pp. 258–269. Springer (2022)
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-Thought prompting elicits reasoning in large language models. In: Advances in Neural Information Processing Systems. vol. 35, pp. 24824–24837 (2022)
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Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., Hsu, J., et al.: RT-1: Robotics transformer for real-world control at scale. Robotics: Science and Systems (2023)
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Brohan, A., Chebotar, Y., Finn, C., Hausman, K., Herzog, A., Ho, D., et al.: Do as I can, not as I say: Grounding language in robotic affordances. In: Conference on Robot Learning (CoRL). pp. 287–318. PMLR (2023)
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Cited alongside, same era.
2023
Cited alongside, same era.
Gutman, D., Olatunji, S.A., Markfeld, N., Givati, S., Sarne-Fleischmann, V., Oron-Gilad, T., Edan, Y.: Evaluating levels of automation with different feedback modes in an assistive robotic table clearing task for eldercare. In: Applied Ergonomics. vol. 106, p. 103859 (2023)
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2023
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2023
Cited alongside, same era.
OpenAI: GPT-4V(ision) system card (2023)
2023
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Özdemir, O., Kerzel, M., Weber, C., Lee, J.H., Wermter, S.: Language model-based paired variational autoencoders for robotic language learning. IEEE Transactions on Cognitive and Developmental Systems 15
2023
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Rana, K., Haviland, J., Garg, S., Abou-Chakra, J., Reid, I., Suenderhauf, N.: SayPlan: Grounding large language models using 3d scene graphs for scalable task planning. In: Conference on Robot Learning (CoRL) (2023)
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Ren, A.Z., Dixit, A., Bodrova, A., Singh, S., Tu, S., Brown, N., et al.: Robots that ask for help: Uncertainty alignment for large language model planners. Conference on Robot Learning (CoRL) (2023)
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2023
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Zhao, X., Li, M., Weber, C., Hafez, M.B., Wermter, S.: Chat with the Environment: Interactive multimodal perception using large language models. In: Conference on Intelligent Robotsand Systems (IROS). pp. 3590–3596 (2023)
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Later among the works it cites.
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Zitkovich, B., Yu, T., Xu, S., Xu, P., Xiao, T., Xia, F., et al.: RT-2: Vision-language-action models transfer web knowledge to robotic control. In: Conference on Robot Learning (CoRL). pp. 2165–2183. PMLR (2023)
2023
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2024
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Minderer, M., Gritsenko, A., Houlsby, N.: Scaling open-vocabulary object detection. In: Advances in Neural Information Processing Systems. vol. 36 (2024)
2024
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Valmeekam, K., Marquez, M., Olmo, A., Sreedharan, S., Kambhampati, S.: PlanBench: an extensible benchmark for evaluating large language models on planning and reasoning about change. In: Advances Neural Information Processing Systems. vol. 36 (2024)
2024
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Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., Narasimhan, K.: Tree of thoughts: Deliberate problem solving with large language models. In: Advances in Neural Information Processing Systems. vol. 36 (2024)
2024
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Zhao, Z., Lee, W.S., Hsu, D.: Large language models as commonsense knowledge for large-scale task planning. Advances in Neural Information Processing Systems 36
2024
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