[go: up one dir, main page]

Tidemann et al., 2007 - Google Patents

Self-organizing multiple models for imitation: Teaching a robot to dance the YMCA

Tidemann et al., 2007

View PDF
Document ID
3589383848441410998
Author
Tidemann A
Öztürk P
Publication year
Publication venue
International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

External Links

Snippet

The traditional approach to implement motor behaviour in a robot required a programmer to carefully decide the joint velocities at each timestep. By using the principle of learning by imitation, the robot can instead be taught simply by showing it what to do. This paper …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • 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
    • G06N3/04—Architectures, e.g. interconnection topology
    • 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
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • 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
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/004—Artificial life, i.e. computers simulating life
    • G06N3/008—Artificial 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
    • 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
    • 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
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Metta et al. Early integration of vision and manipulation.
Dearden et al. Learning forward models for robots
Montesano et al. Learning object affordances: from sensory--motor coordination to imitation
Chen et al. Human-in-the-loop robot learning for smart manufacturing: A human-centric perspective
Metta et al. Better vision through manipulation
Yanik et al. A gesture learning interface for simulated robot path shaping with a human teacher
Jin et al. Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach
Mici et al. An incremental self-organizing architecture for sensorimotor learning and prediction
Tani et al. Achieving “organic compositionality” through self-organization: reviews on brain-inspired robotics experiments
Yokoya et al. Experience-based imitation using RNNPB
Jiang et al. A data-efficient goal-directed deep reinforcement learning method for robot visuomotor skill
Tidemann et al. Self-organizing multiple models for imitation: Teaching a robot to dance the YMCA
Liu et al. Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neuroscience
de Rengervé et al. Emergent imitative behavior on a robotic arm based on visuo-motor associative memories
Dursun et al. Imitation learning for autonomous driving: insights from real-world testing
Kopp et al. Imitation learning and response facilitation in embodied agents
Guan et al. Review of the techniques used in motor‐cognitive human‐robot skill transfer
Kajic et al. A biologically inspired model for coding sensorimotor experience leading to the development of pointing behaviour in a humanoid robot
Takamuku et al. Multi-method learning and assimilation
Oztop et al. Imitation bootstrapping: Experiments on a robotic hand
Rojas-Castro et al. RHIZOME ARCHITECTURE: An adaptive neurobehavioral control architecture for cognitive mobile robots—Application in a vision-based indoor robot navigation context
Ushida et al. Fuzzy-associative-memory-based knowledge construction with an application to a human-machine interface
Kubota et al. The role of spiking neurons for visual perception of a partner robot
Takamuku et al. Lexicon acquisition based on object-oriented behavior learning
Kulić et al. Incremental learning and memory consolidation of whole body human motion primitives