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RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration., , , , and . IEEE Robotics Autom. Lett., 5 (4): 6262-6269 (2020)APPL: Adaptive Planner Parameter Learning., , , , , , , and . CoRR, (2021)APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback., , , and . CoRR, (2021)Sample-efficient Adversarial Imitation Learning from Observation., , , and . CoRR, (2019)Adversarial Imitation Learning from State-only Demonstrations., , and . AAMAS, page 2229-2231. International Foundation for Autonomous Agents and Multiagent Systems, (2019)DIPD: Gaze-Based Intention Inference in Dynamic Environments., , and . AAAI Workshops, volume WS-18 of AAAI Technical Report, page 614-621. AAAI Press, (2018)Machine Learning Methods for Local Motion Planning: A Study of End-to-End vs. Parameter Learning., , , , and . SSRR, page 217-222. IEEE, (2021)D-Shape: Demonstration-Shaped Reinforcement Learning via Goal-Conditioning., , and . AAMAS, page 1267-1275. ACM, (2023)STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience., , , , , and . CoRL, volume 229 of Proceedings of Machine Learning Research, page 2393-2413. PMLR, (2023)APPLR: Adaptive Planner Parameter Learning from Reinforcement., , , , , , , and . ICRA, page 6086-6092. IEEE, (2021)