Zhou et al., 2020 - Google Patents
Incremental learning robot task representation and identificationZhou et al., 2020
View HTML- Document ID
- 18354788175399771504
- Author
- Zhou X
- Wu H
- Rojas J
- Xu Z
- Li S
- Publication year
- Publication venue
- Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
External Links
Snippet
In this chapter, we present a novel method for incremental learning robot complex task representation, identifying repeated skills, and generalizing to new environment by heuristically segmenting the unstructured demonstrations into movement primitives that …
- 238000005183 dynamical system 0 abstract description 6
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/39—Robotics, robotics to robotics hand
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/40—Robotics, robotics mapping to robotics vision
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
-
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
-
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kyrarini et al. | Robot learning of industrial assembly task via human demonstrations | |
| Ravichandar et al. | Learning Partially Contracting Dynamical Systems from Demonstrations. | |
| US20240351209A1 (en) | A robot system for anomaly detection | |
| Forte et al. | On-line motion synthesis and adaptation using a trajectory database | |
| Lee et al. | Mimetic communication model with compliant physical contact in human—humanoid interaction | |
| JP7808295B2 (en) | Method for controlling a robotic device | |
| Ravichandar et al. | Learning position and orientation dynamics from demonstrations via contraction analysis | |
| Wu et al. | A framework of robot skill learning from complex and long-horizon tasks | |
| Havoutis et al. | Supervisory teleoperation with online learning and optimal control | |
| Tavassoli et al. | Learning skills from demonstrations: A trend from motion primitives to experience abstraction | |
| Ghalyan | Force-Controlled Robotic Assembly Processes of Rigid and Flexible Objects | |
| Colomé et al. | Reinforcement learning of bimanual robot skills | |
| Wu et al. | Incremental learning introspective movement primitives from multimodal unstructured demonstrations | |
| Zhou et al. | Incremental learning robot task representation and identification | |
| Wang et al. | Deep-learning-based object classification of tactile robot hand for smart factory: D. Wang et al. | |
| Wu et al. | Robot introspection with bayesian nonparametric vector autoregressive hidden markov models | |
| Navarro-Gonzalez et al. | On-line incremental learning for unknown conditions during assembly operations with industrial robots | |
| Nemec et al. | Learning of exception strategies in assembly tasks | |
| Girgin et al. | Associative skill memory models | |
| Skoglund et al. | Programming-by-Demonstration of reaching motions—A next-state-planner approach | |
| Park et al. | Collision Detection for Robot Manipulators: Methods and Algorithms | |
| Zahedi et al. | Gesture-based adaptive haptic guidance: a comparison of discriminative and generative modeling approaches | |
| Hersch et al. | Learning dynamical system modulation for constrained reaching tasks | |
| Ewerton et al. | Reinforcement learning of trajectory distributions: Applications in assisted teleoperation and motion planning | |
| Koropouli et al. | Generalization of Force Control Policies from Demonstrations for Constrained Robotic Motion Tasks: A Regression-Based Approach |