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SHIV: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces.

, , , , , , and . ICRA, page 462-469. IEEE, (2016)

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Preferences Implicit in the State of the World., , , , and . ICLR (Poster), OpenReview.net, (2019)SQIL: Imitation Learning via Regularized Behavioral Cloning., , and . CoRR, (2019)A Robust Control Framework for Human Motion Prediction., , , , and . IEEE Robotics Autom. Lett., 6 (1): 24-31 (2021)Learning Optimal Advantage from Preferences and Mistaking it for Reward., , , , , , and . CoRR, (2023)Manipulation planning with goal sets using constrained trajectory optimization., , and . ICRA, page 4582-4588. IEEE, (2011)Expressive Robot Motion Timing., , , and . CoRR, (2018)Assistive teleoperation for manipulation tasks., and . HRI, page 123-124. ACM, (2012)Probabilistically Safe Robot Planning with Confidence-Based Human Predictions., , , , , , and . Robotics: Science and Systems, (2018)Generating Plans that Predict Themselves., , , , , , and . WAFR, volume 13 of Springer Proceedings in Advanced Robotics, page 144-159. Springer, (2016)Learning Representations that Enable Generalization in Assistive Tasks., , , , and . CoRL, volume 205 of Proceedings of Machine Learning Research, page 2105-2114. PMLR, (2022)