Ettehadi et al., 2018 - Google Patents

A learning from demonstration framework for implementation of a feeding task

Ettehadi et al., 2018

Document ID
16389965018480090034
Author
Ettehadi N
Behal A
Publication year
Publication venue
Encyclopedia with Semantic Computing and Robotic Intelligence

External Links

Snippet

In this paper, a learning from demonstration (LFD) approach is used to design an autonomous meal-assistance agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of the …
Continue reading at www.worldscientific.com (other versions)

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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions

Similar Documents

Publication Publication Date Title
Maeda et al. Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks
Rana et al. Towards robust skill generalization: Unifying learning from demonstration and motion planning
Pérez-D'Arpino et al. C-learn: Learning geometric constraints from demonstrations for multi-step manipulation in shared autonomy
Qu et al. Human-like coordination motion learning for a redundant dual-arm robot
Krishnan et al. SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards
Calinon et al. Incremental learning of gestures by imitation in a humanoid robot
Calinon et al. Statistical learning by imitation of competing constraints in joint space and task space
Kopicki et al. Learning modular and transferable forward models of the motions of push manipulated objects
Balaguer et al. Combining imitation and reinforcement learning to fold deformable planar objects
Ravichandar et al. Learning Partially Contracting Dynamical Systems from Demonstrations.
Elbasiony et al. Humanoids skill learning based on real-time human motion imitation using Kinect
Kim et al. Gaussian mixture model for 3-dof orientations
Field et al. Learning trajectories for robot programing by demonstration using a coordinated mixture of factor analyzers
Vogt et al. One-shot learning of human–robot handovers with triadic interaction meshes
Pozzi et al. Hand closure model for planning top grasps with soft robotic hands
Kappler et al. Templates for pre-grasp sliding interactions
Ettehadi et al. A learning from demonstration framework for implementation of a feeding task
Chen et al. Learning human-robot collaboration insights through the integration of muscle activity in interaction motion models
Balmik et al. Nao robot teleoperation with human motion recognition
Rosado et al. A Kinect-based motion capture system for robotic gesture imitation
Lloyd et al. Pose-and-shear-based tactile servoing
Ettehadi et al. Implementation of feeding task via learning from demonstration
Palm et al. Recognition of human grasps by time-clustering and fuzzy modeling
Skoglund et al. Programming-by-Demonstration of reaching motions—A next-state-planner approach
Iodice et al. Learning cooperative dynamic manipulation skills from human demonstration videos