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Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation.

, , , , and . IROS, page 2629-2634. IEEE, (2019)

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AssistOn-Mobile: a series elastic holonomic mobile platform for upper extremity rehabilitation., , , and . Robotica, 32 (8): 1433-1459 (2014)Slacking prevention during assistive contour following tasks with guaranteed coupled stability., and . IROS, page 1587-1594. IEEE, (2012)Brain Computer Interface based robotic rehabilitation with online modification of task speed., , , , and . ICORR, page 1-7. IEEE, (2013)The effect of robotic wheelchair control paradigm and interface on user performance, effort and preference: An experimental assessment., and . Robotics Auton. Syst., (2017)An Analysis of Degraded Communication Channels in Human-Robot Teaming and Implications for Dynamic Autonomy Allocation., , , and . FSR, volume 5 of Springer Proceedings in Advanced Robotics, page 665-679. Springer, (2017)Causal Reasoning for Planning and Coordination of Multiple Housekeeping Robots., , , and . LPNMR, volume 6645 of Lecture Notes in Computer Science, page 311-316. Springer, (2011)Control of a BCI-based upper limb rehabilitation system utilizing posterior probabilities., , , , and . SIU, page 1-4. IEEE, (2013)Online generation of velocity fields for passive contour following., and . World Haptics, page 245-250. IEEE Computer Society, (2011)Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation., , , , and . CoRR, (2020)Prediction of user preference over shared-control paradigms for a robotic wheelchair., and . ICORR, page 1106-1111. IEEE, (2017)