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 image

Kim 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 …
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Classifications

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    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
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    • G06K9/00221Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268Feature extraction; Face representation
    • G06K9/00281Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • GPHYSICS
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    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
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    • G06K9/6288Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
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    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
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    • G06COMPUTING; CALCULATING; COUNTING
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    • G06K9/00362Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand

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