Text2Motion: from natural language instructions to feasible plans

Author:

Lin KevinORCID,Agia ChristopherORCID,Migimatsu TokiORCID,Pavone MarcoORCID,Bohg JeannetteORCID

Funder

Toyota Research Institute

National Aeronautics and Space Administration

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference76 articles.

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2. Agia, C., Migimatsu, T., Wu, J., & Bohg, J. (2022). STAP: Sequencing task-agnostic policies. arXiv preprint arXiv:2210.12250

3. Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., et al. (2022). Do as i can, not as I say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691

4. Ames, B., Thackston, A., & Konidaris, G. (2018). Learning symbolic representations for planning with parameterized skills. In 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 526–533). IEEE.

5. Bidot, J., Karlsson, L., Lagriffoul, F., & Saffiotti, A. (2017). Geometric backtracking for combined task and motion planning in robotic systems. Artificial Intelligence, 247, 229–265.

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