Mitrovic, 2011 - Google Patents
Stochastic optimal control with learned dynamics modelsMitrovic, 2011
View PDF- Document ID
- 5212925457862610130
- Author
- Mitrovic D
- Publication year
External Links
Snippet
The motor control of anthropomorphic robotic systems is a challenging computational task mainly because of the high levels of redundancies such systems exhibit. Optimality principles provide a general strategy to resolve such redundancies in a task driven fashion …
- 238000004805 robotic 0 abstract description 36
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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/40—Robotics, robotics mapping to robotics vision
-
- 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
- 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
-
- 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
-
- 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
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mitrovic et al. | Adaptive optimal feedback control with learned internal dynamics models | |
| Calinon et al. | Learning control | |
| Gribovskaya et al. | Learning non-linear multivariate dynamics of motion in robotic manipulators | |
| Atkeson et al. | Locally weighted learning for control | |
| Tanwani et al. | A generative model for intention recognition and manipulation assistance in teleoperation | |
| Khansari-Zadeh et al. | Imitation learning of globally stable non-linear point-to-point robot motions using nonlinear programming | |
| Ghadirzadeh et al. | A sensorimotor reinforcement learning framework for physical human-robot interaction | |
| Buchli et al. | Variable impedance control a reinforcement learning approach | |
| CN109702740B (en) | Robot compliance control method, device, equipment and storage medium | |
| Krug et al. | Model predictive motion control based on generalized dynamical movement primitives | |
| Peters et al. | Robot learning | |
| CN117140527A (en) | A robotic arm control method and system based on deep reinforcement learning algorithm | |
| Silvério et al. | Uncertainty-aware imitation learning using kernelized movement primitives | |
| Mitrovic et al. | Learning impedance control of antagonistic systems based on stochastic optimization principles | |
| WO2020118730A1 (en) | Compliance control method and apparatus for robot, device, and storage medium | |
| Ratliff et al. | Doomed: Direct online optimization of modeling errors in dynamics | |
| Nemec et al. | Speed adaptation for self-improvement of skills learned from user demonstrations | |
| Polydoros et al. | Online multi-target learning of inverse dynamics models for computed-torque control of compliant manipulators | |
| Wu et al. | Adaptive impedance control based on reinforcement learning in a human-robot collaboration task with human reference estimation | |
| Kiemel et al. | Learning robot trajectories subject to kinematic joint constraints | |
| Krug et al. | Representing movement primitives as implicit dynamical systems learned from multiple demonstrations | |
| Lober et al. | Efficient reinforcement learning for humanoid whole-body control | |
| Mamedov et al. | Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots | |
| Nordmann et al. | Teaching nullspace constraints in physical human-robot interaction using reservoir computing | |
| Joukov et al. | Gaussian process based model predictive controller for imitation learning |