Wang et al., 2023 - Google Patents
Dexgraspnet: A large-scale robotic dexterous grasp dataset for general objects based on simulationWang et al., 2023
View PDF- Document ID
- 17374432468210982460
- Author
- Wang R
- Zhang J
- Chen J
- Xu Y
- Li P
- Liu T
- Wang H
- Publication year
- Publication venue
- 2023 IEEE International Conference on Robotics and Automation (ICRA)
External Links
Snippet
Robotic dexterous grasping is the first step to enable human-like dexterous object manipulation and thus a crucial robotic technology. However, dexterous grasping is much more under-explored than object grasping with parallel grippers, partially due to the lack of a …
- 238000004088 simulation 0 title description 6
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- 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
- G06K9/6247—Extracting 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wang et al. | Dexgraspnet: A large-scale robotic dexterous grasp dataset for general objects based on simulation | |
Newbury et al. | Deep learning approaches to grasp synthesis: A review | |
Sundermeyer et al. | Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes | |
Corona et al. | Ganhand: Predicting human grasp affordances in multi-object scenes | |
Karunratanakul et al. | Grasping field: Learning implicit representations for human grasps | |
Wang et al. | Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions | |
Lundell et al. | Ddgc: Generative deep dexterous grasping in clutter | |
Lundell et al. | Robust grasp planning over uncertain shape completions | |
Turpin et al. | Grasp’d: Differentiable contact-rich grasp synthesis for multi-fingered hands | |
Aleotti et al. | Part-based robot grasp planning from human demonstration | |
Simeonov et al. | A long horizon planning framework for manipulating rigid pointcloud objects | |
Lou et al. | Collision-aware target-driven object grasping in constrained environments | |
Kiatos et al. | A geometric approach for grasping unknown objects with multifingered hands | |
Wang et al. | DemoGrasp: Few-shot learning for robotic grasping with human demonstration | |
Mayer et al. | FFHNet: Generating multi-fingered robotic grasps for unknown objects in real-time | |
Yang et al. | Attribute-based robotic grasping with one-grasp adaptation | |
Simão et al. | Natural control of an industrial robot using hand gesture recognition with neural networks | |
Valarezo Anazco et al. | Natural object manipulation using anthropomorphic robotic hand through deep reinforcement learning and deep grasping probability network | |
Khargonkar et al. | Neuralgrasps: Learning implicit representations for grasps of multiple robotic hands | |
Devgon et al. | Orienting novel 3D objects using self-supervised learning of rotation transforms | |
Dong et al. | A review of robotic grasp detection technology | |
Zhang et al. | Affordance-driven next-best-view planning for robotic grasping | |
Gao et al. | Variational object-aware 3-d hand pose from a single rgb image | |
Yang et al. | Autonomous tool construction with gated graph neural network | |
Rustler et al. | Efficient visuo-haptic object shape completion for robot manipulation |