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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection., , , , and . Int. J. Robotics Res., 37 (4-5): 421-436 (2018)QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation., , , , , , , , , and 1 other author(s). CoRR, (2018)Fast, robust quadruped locomotion over challenging terrain., , , , and . ICRA, page 2665-2670. IEEE, (2010)Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection., , , and . CoRR, (2016)Do As I Can, Not As I Say: Grounding Language in Robotic Affordances., , , , , , , , , and 33 other author(s). CoRR, (2022)How to train your robot with deep reinforcement learning: lessons we have learned., , , , , and . Int. J. Robotics Res., (2021)Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection., , , and . ISER, volume 1 of Springer Proceedings in Advanced Robotics, page 173-184. Springer, (2016)Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping., , , and . ICRA, page 4804-4810. IEEE, (2019)Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping., , , , and . CoRR, (2019)End-to-End Learning of Semantic Grasping., , , , and . CoRL, volume 78 of Proceedings of Machine Learning Research, page 119-132. PMLR, (2017)