[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Oguz et al., 2017 - Google Patents

Hybrid human motion prediction for action selection within human-robot collaboration

Oguz et al., 2017

View PDF
Document ID
8340375639686049912
Author
Oguz O
Gabler V
Huber G
Zhou Z
Wollherr D
Publication year
Publication venue
2016 International Symposium on Experimental Robotics

External Links

Snippet

Abstract We present a Human-Robot-Collaboration (HRC) framework consisting of a hybrid human motion prediction approach together with a game theoretical action selection. In essence, the robot is required to predict the motions of the human co-worker, and to …
Continue reading at www.researchgate.net (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
    • 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
    • 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
    • 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
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • 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
    • 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
    • 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
    • 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
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational 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
    • 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

Similar Documents

Publication Publication Date Title
Lowrey et al. Plan online, learn offline: Efficient learning and exploration via model-based control
Muratore et al. Assessing transferability from simulation to reality for reinforcement learning
Billard et al. Learning from humans
Shahid et al. Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning
Mendonça et al. Autonomous navigation system using event driven-fuzzy cognitive maps
Abdolmaleki et al. Learning a humanoid kick with controlled distance
Zwilling et al. Simulation for the RoboCup logistics league with real-world environment agency and multi-level abstraction
Krivic et al. Pushing corridors for delivering unknown objects with a mobile robot
Shukla et al. Robotic grasp manipulation using evolutionary computing and deep reinforcement learning
Oguz et al. Hybrid human motion prediction for action selection within human-robot collaboration
Pérez-Dattari et al. Stable motion primitives via imitation and contrastive learning
Kargin et al. A reinforcement learning approach for continuum robot control
Kasaei et al. Data-efficient non-parametric modelling and control of an extensible soft manipulator
Ordaz-Rivas et al. Collective tasks for a flock of robots using influence factor
Bertolucci et al. An ASP-based framework for the manipulation of articulated objects using dual-arm robots
Nambiar et al. Automation of unstructured production environment by applying reinforcement learning
Blinov et al. Deep Q-learning algorithm for solving inverse kinematics of four-link manipulator
Wang et al. Learning adaptive reaching and pushing skills using contact information
Gillawat et al. Human upper limb joint torque minimization using genetic algorithm
Hamandi et al. Predicting the target in human-robot manipulation tasks
Kim et al. Generalizing over uncertain dynamics for online trajectory generation
Ben Hazem Study of Q-learning and deep Q-network learning control for a rotary inverted pendulum system
Zindler et al. Towards Dynamic Obstacle Avoidance for Robot Manipulators with Deep Reinforcement Learning
Younes et al. Toward faster reinforcement learning for robotics: using Gaussian processes
Muhayyuddin et al. Knowledge-Oriented Physics-Based Motion Planning for Grasping Under Uncertainty