[go: up one dir, main page]

Gams et al., 2015 - Google Patents

Learning of parametric coupling terms for robot-environment interaction

Gams et al., 2015

View PDF
Document ID
15273820491937932206
Author
Gams A
Denisa M
Ude A
Publication year
Publication venue
2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)

External Links

Snippet

In order to be effective, learning of robotic motion by demonstration should not remain limited to direct repetition of movements, but should enable modifications with respect to the state of the external environment, and generation of actions for previously unencountered …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • 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
    • 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
    • G05B13/042—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 in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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
    • 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
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/19—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by positioning or contouring control systems, e.g. to control position from one programmed point to another or to control movement along a programmed continuous path
    • 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
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Yang et al. Neural networks enhanced adaptive admittance control of optimized robot–environment interaction
Gribovskaya et al. Learning non-linear multivariate dynamics of motion in robotic manipulators
Rozo et al. Learning optimal controllers in human-robot cooperative transportation tasks with position and force constraints
Calinon et al. Learning collaborative manipulation tasks by demonstration using a haptic interface
Khansari-Zadeh et al. Imitation learning of globally stable non-linear point-to-point robot motions using nonlinear programming
Pastor et al. From dynamic movement primitives to associative skill memories
Forte et al. On-line motion synthesis and adaptation using a trajectory database
Franzese et al. Interactive imitation learning of bimanual movement primitives
Bischoff et al. Policy search for learning robot control using sparse data
Gams et al. Learning of parametric coupling terms for robot-environment interaction
Paraschos et al. Model-free probabilistic movement primitives for physical interaction
Zeestraten et al. Variable duration movement encoding with minimal intervention control
Mathew et al. Online learning of feed-forward models for task-space variable impedance control
Umlauft et al. Bayesian uncertainty modeling for programming by demonstration
Schperberg et al. Real-to-sim: Predicting residual errors of robotic systems with sparse data using a learning-based unscented kalman filter
Girgin et al. Associative skill memory models
Hu et al. Mobile robot learning from human demonstrations with nonlinear model predictive control
Oikonomou et al. Task driven skill learning in a soft-robotic arm
Petrič et al. Bio-inspired learning and database expansion of compliant movement primitives
Hu et al. Neural learning of stable dynamical systems based on extreme learning machine
Ewerton et al. Reinforcement learning of trajectory distributions: Applications in assisted teleoperation and motion planning
Koropouli et al. Generalization of Force Control Policies from Demonstrations for Constrained Robotic Motion Tasks: A Regression-Based Approach
Oikonomou et al. Periodic movement learning in a soft-robotic arm
Gams et al. Learning task-specific dynamics to improve whole-body control
Schperberg et al. Real-to-sim: Deep learning with auto-tuning to predict residual errors using sparse data