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Towards vision-based deep reinforcement learning for robotic motion control
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TLDR
In this article, a Deep Q Network (DQNets) was used to learn target reaching with a three-joint robot manipulator using external visual observation, which was demonstrated to perform target reaching after training in simulation.Abstract:
This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.read more
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Proceedings ArticleDOI
Robust Physical-World Attacks on Deep Learning Visual Classification
Kevin Eykholt,Ivan Evtimov,Earlence Fernandes,Bo Li,Amir Rahmati,Chaowei Xiao,Atul Prakash,Tadayoshi Kohno,Dawn Song +8 more
TL;DR: This work proposes a general attack algorithm, Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions and shows that adversarial examples generated using RP2 achieve high targeted misclassification rates against standard-architecture road sign classifiers in the physical world under various environmental conditions, including viewpoints.
Posted Content
Robust Physical-World Attacks on Deep Learning Models
Ivan Evtimov,Kevin Eykholt,Earlence Fernandes,Tadayoshi Kohno,Bo Li,Atul Prakash,Amir Rahmati,Dawn Song +7 more
TL;DR: This work proposes a general attack algorithm,Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions and shows that adversarial examples generated using RP2 achieve high targeted misclassification rates against standard-architecture road sign classifiers in the physical world under various environmental conditions, including viewpoints.
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Runtime Neural Pruning
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DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Lei Ma,Felix Juefei-Xu,Fuyuan Zhang,Jiyuan Sun,Minhui Xue,Bo Li,Chunyang Chen,Ting Su,Li Li,Yang Liu,Jianjun Zhao,Yadong Wang +11 more
TL;DR: DeepGauge as discussed by the authors proposes a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed.
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Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
TL;DR: This paper proposes a simulated benchmark for robotic grasping that emphasizes off-policy learning and generalization to unseen objects, and indicates that several simple methods provide a surprisingly strong competitor to popular algorithms such as double Q-learning.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
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Posted Content
Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Alex Graves,Ioannis Antonoglou,Daan Wierstra,Martin Riedmiller +6 more
TL;DR: This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
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Mechanics of Robotic Manipulation
TL;DR: Mechanics of Robotic Manipulation addresses one form of robotic manipulation, moving objects, and the various processes involved---grasping, carrying, pushing, dropping, throwing, and so on.