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Recent trends in robot arm control have seen a shift towards end-to-end solutions, using deep reinforcement learning to learn a controller directly from raw sensor data, rather than relying on a hand-crafted, modular pipeline.
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Chelsea Finn et al. "Deep Spatial Autoencoders for Visuomotor Learning." ICRA 2016
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Sergey Levine et al. "Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection." ISER 2016
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Shixiang Gu et al. "Deep Reinforcement Learning for Robotic Manipulation." arXiv preprint 2016
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Edward Johns, Stefan Leutenegger, and Andrew J. Davison. "Pairwise Decomposition of Image Sequences for Active Multi-View Recognition." CVPR 2016
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Eric Tzeng et al. "Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments." WARF 2016
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Andrei A. Rusu et al. "Sim-to-Real Robot Learning from Pixels with Progressive Nets." arXiv preprint 2016
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Timothy P. Lillicrap et al. "Continuous control with deep reinforcement learning." ICLR 2016
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Ankur Handa et al. "Understanding real world indoor scenes with synthetic data." CVPR 2016
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Andrei A. Rusu et al. "Progressive neural networks." arXiv preprint 2016
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Edward Johns, Stefan Leutenegger, and Andrew J. Davison. "Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty." IROS 2016
2016
Cited alongside, same era.
2016
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