[go: up one dir, main page]

Tavassoli et al., 2023 - Google Patents

Learning skills from demonstrations: A trend from motion primitives to experience abstraction

Tavassoli et al., 2023

View PDF
Document ID
17650536117511002703
Author
Tavassoli M
Katyara S
Pozzi M
Deshpande N
Caldwell D
Prattichizzo D
Publication year
Publication venue
IEEE Transactions on Cognitive and Developmental Systems

External Links

Snippet

The uses of robots are changing from static environments in factories to encompass novel concepts such as human–robot collaboration in unstructured settings. Preprogramming all the functionalities for robots becomes impractical, and hence, robots need to learn how to …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • 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
    • 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
    • G06N3/08—Learning methods
    • 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
    • G05B13/027—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 using neural networks only
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • 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
    • 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
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines

Similar Documents

Publication Publication Date Title
Ibarz et al. How to train your robot with deep reinforcement learning: lessons we have learned
Tavassoli et al. Learning skills from demonstrations: A trend from motion primitives to experience abstraction
Pignat et al. Bayesian Gaussian mixture model for robotic policy imitation
Billard et al. Learning from humans
Calinon et al. Learning control
Billard et al. Robot programming by demonstration
Petschnigg et al. Quantum computation in robotic science and applications
Ravichandar et al. Learning position and orientation dynamics from demonstrations via contraction analysis
Billing et al. A formalism for learning from demonstration
Do et al. Learn to wipe: A case study of structural bootstrapping from sensorimotor experience
Wu et al. A framework of robot skill learning from complex and long-horizon tasks
Carvalho et al. Motion planning diffusion: Learning and adapting robot motion planning with diffusion models
Tanwani et al. Generalizing robot imitation learning with invariant hidden semi-Markov models
Xue et al. Logic-skill programming: An optimization-based approach to sequential skill planning
Tanwani Generative models for learning robot manipulation skills from humans
Bai et al. Towards a unified understanding of robot manipulation: A comprehensive survey
Vakanski et al. Robot learning by visual observation
Wong Towards lifelong self-supervision: A deep learning direction for robotics
Calinon Continuous extraction of task constraints in a robot programming by demonstration framework
Guan et al. Review of the techniques used in motor‐cognitive human‐robot skill transfer
Jankowski et al. Robust pushing: Exploiting quasi-static belief dynamics and contact-informed optimization
Stramandinoli et al. Heteroscedastic regression and active learning for modeling affordances in humanoids
Jetchev et al. Discovering relevant task spaces using inverse feedback control
Huang et al. Behavior-based Task Learning by Demonstration for Mobile Manipulators
Moezzi Towards Sample-Efficient Reinforcement Learning Methods for Robotic Manipulation Tasks