Gao et al., 2021 - Google Patents
Online learning in planar pushing with combined prediction modelGao et al., 2021
View PDF- Document ID
- 5219096616395373533
- Author
- Gao H
- Ouyang Y
- Tomizuka M
- Publication year
- Publication venue
- 2021 American Control Conference (ACC)
External Links
Snippet
Pushing is a useful robotic capability for positioning and reorienting objects. The ability to accurately predict the effect of pushes can enable efficient trajectory planning and complicated object manipulation. Physical prediction models for planar pushing have long …
- 230000001537 neural 0 abstract description 23
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
-
- 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
-
- 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
- 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
- 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
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
-
- 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 |
|---|---|---|
| Huang et al. | Continual model-based reinforcement learning with hypernetworks | |
| Florence et al. | Self-supervised correspondence in visuomotor policy learning | |
| Zhang et al. | Learning variable impedance control via inverse reinforcement learning for force-related tasks | |
| Stouraitis et al. | Online hybrid motion planning for dyadic collaborative manipulation via bilevel optimization | |
| Calinon et al. | A task-parameterized probabilistic model with minimal intervention control | |
| CN115351780A (en) | Method for controlling a robotic device | |
| Hoppe et al. | Planning approximate exploration trajectories for model-free reinforcement learning in contact-rich manipulation | |
| Melingui et al. | Adaptive algorithms for performance improvement of a class of continuum manipulators | |
| Paus et al. | Predicting pushing action effects on spatial object relations by learning internal prediction models | |
| Serrano-Munoz et al. | Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study | |
| Kobayashi et al. | ILBiT: Imitation learning for robot using position and torque information based on bilateral control with transformer | |
| Kasaei et al. | Data-efficient non-parametric modelling and control of an extensible soft manipulator | |
| Naveed et al. | Adaptive trajectory tracking of wheeled mobile robot with uncertain parameters | |
| Mahmoodabadi et al. | Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode‐Based Feedback Linearization Controller | |
| Gao et al. | Online learning in planar pushing with combined prediction model | |
| Kasaei et al. | A data-efficient neural ODE framework for optimal control of soft manipulators | |
| Shen et al. | Review on Peg-in-Hole Insertion Technology Based on Reinforcement Learning | |
| Cao et al. | Shape control of elastic deformable linear objects for robotic cable assembly | |
| Totsila et al. | Sensorimotor Learning With Stability Guarantees via Autonomous Neural Dynamic Policies | |
| Boas et al. | A dmps-based approach for human-robot collaboration task quality management | |
| Hu et al. | Neural learning of stable dynamical systems based on extreme learning machine | |
| CN107894709A (en) | Controlled based on Adaptive critic network redundancy Robot Visual Servoing | |
| Man et al. | Robot bolt skill learning based on GMM-GMR | |
| Djelal et al. | LSTM-Based Visual Control for Complex Robot Interactions. | |
| Hogan | Reactive manipulation with contact models and tactile feedback |