Kim et al., 2021 - Google Patents
Acceleration of actor-critic deep reinforcement learning for visual grasping by state representation learning based on a preprocessed input imageKim et al., 2021
- Document ID
- 17099694634859641845
- Author
- Kim T
- Park Y
- Park Y
- Lee S
- Suh I
- Publication year
- Publication venue
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
External Links
Snippet
For robotic grasping tasks with diverse target objects, some deep learning-based methods have achieved state-of-the-art results using direct visual input. In contrast, actor-critic deep reinforcement learning (RL) methods typically perform very poorly when applied to grasp …
- 230000000007 visual effect 0 title abstract description 20
Classifications
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
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- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
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- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
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- G—PHYSICS
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- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
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- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
- G06K9/629—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
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- G—PHYSICS
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- G06N3/00—Computer systems based on biological models
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