[go: up one dir, main page]

Saveriano et al., 2023 - Google Patents

Dynamic movement primitives in robotics: A tutorial survey

Saveriano et al., 2023

View PDF
Document ID
8697590198601816529
Author
Saveriano M
Abu-Dakka F
Kramberger A
Peternel L
Publication year
Publication venue
The International Journal of Robotics Research

External Links

Snippet

Biological systems, including human beings, have the innate ability to perform complex tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to comprehend and formally define this innate characteristic. The idea, supported by several …
Continue reading at journals.sagepub.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • 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
    • G06K9/6217Design 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/004Artificial life, i.e. computers simulating life
    • G06N3/008Artificial life, i.e. computers simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. robots replicating pets or humans in their appearance or behavior
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • 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
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive 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

Similar Documents

Publication Publication Date Title
Saveriano et al. Dynamic movement primitives in robotics: A tutorial survey
Calinon et al. Learning control
Ravichandar et al. Recent advances in robot learning from demonstration
Gu et al. Humanoid locomotion and manipulation: Current progress and challenges in control, planning, and learning
Billard et al. Robot programming by demonstration
Calinon et al. Incremental learning of gestures by imitation in a humanoid robot
Jenkins et al. Performance-derived behavior vocabularies: Data-driven acquisition of skills from motion
Shaik et al. Adaptive Control Through Reinforcement Learning: Robotic Systems in Action
Field et al. Learning trajectories for robot programing by demonstration using a coordinated mixture of factor analyzers
Sreenivasa et al. Walking to grasp: Modeling of human movements as invariants and an application to humanoid robotics
Tavassoli et al. Learning skills from demonstrations: A trend from motion primitives to experience abstraction
Nah et al. Robot control based on motor primitives: A comparison of two approaches
Tanwani Generative models for learning robot manipulation skills from humans
Tanwani et al. Generalizing robot imitation learning with invariant hidden semi-Markov models
Gams et al. Manipulation learning on humanoid robots
Asfour et al. The karlsruhe ARMAR humanoid robot family
Skoglund et al. Programming-by-Demonstration of reaching motions—A next-state-planner approach
Alibeigi et al. A fast, robust, and incremental model for learning high-level concepts from human motions by imitation
Behera et al. Intelligent control of robotic systems
Wong Towards lifelong self-supervision: A deep learning direction for robotics
Guan et al. Review of the techniques used in motor‐cognitive human‐robot skill transfer
Maeda et al. Phase portraits as movement primitives for fast humanoid robot control
Kalaria et al. Dreamcontrol: Human-inspired whole-body humanoid control for scene interaction via guided diffusion
Zhu Robot Learning Assembly Tasks from Human Demonstrations
Dani et al. Learning first principles systems knowledge from data: Stability and safety with applications to learning from demonstration