Liu et al., 2025 - Google Patents
Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based CurriculumLiu et al., 2025
View PDF- Document ID
- 2993330214143607096
- Author
- Liu Y
- Kang D
- Ha S
- Publication year
- Publication venue
- arXiv preprint arXiv:2502.12599
External Links
Snippet
Autonomous robotic wiping is an important task in various industries, ranging from industrial manufacturing to sanitization in healthcare. Deep reinforcement learning (Deep RL) has emerged as a promising algorithm, however, it often suffers from a high demand for …
- 238000004458 analytical method 0 title description 6
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
- G05B13/04—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
- 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
- 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
- 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
- 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
- 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
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based 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
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer 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
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bıyık et al. | Active preference-based gaussian process regression for reward learning | |
| Franceschetti et al. | Robotic arm control and task training through deep reinforcement learning | |
| Fang et al. | Survey of imitation learning for robotic manipulation | |
| Sutton et al. | Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction | |
| Jia et al. | A coach-based bayesian reinforcement learning method for snake robot control | |
| Wu et al. | A framework of robot skill learning from complex and long-horizon tasks | |
| Tavassoli et al. | Learning skills from demonstrations: A trend from motion primitives to experience abstraction | |
| Stan et al. | Reinforcement learning for assembly robots: A review | |
| Abbas et al. | Review of intelligent control systems with robotics | |
| KR20230157488A (en) | Multi-objective reinforcement learning using weighted policy projection | |
| Colomé et al. | Reinforcement learning of bimanual robot skills | |
| Sleiman et al. | Guided reinforcement learning for robust multi-contact loco-manipulation | |
| Vinogradska et al. | Numerical quadrature for probabilistic policy search | |
| Ficuciello | Hand-arm autonomous grasping: Synergistic motions to enhance the learning process | |
| Shah et al. | An overview of inverse reinforcement learning techniques | |
| Toussaint et al. | Probabilistic inference for structured planning in robotics | |
| Liu et al. | Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based Curriculum | |
| Behera et al. | Intelligent control of robotic systems | |
| Liu et al. | A policy searched-based optimization algorithm for obstacle avoidance in robot manipulators | |
| Sturm | Approaches to probabilistic model learning for mobile manipulation robots | |
| Langsfeld | Learning task models for robotic manipulation of nonrigid objects | |
| Vidaković et al. | Accelerating robot trajectory learning for stochastic tasks | |
| Koropouli et al. | Generalization of Force Control Policies from Demonstrations for Constrained Robotic Motion Tasks: A Regression-Based Approach | |
| De Risi et al. | Variable Impedance Control Combining Reinforcement Learning and Gaussian Process Regression | |
| Emami et al. | Survey of Multi-Agent Reinforcement Learning to Solve Inverse Kinematic Problems of Redundant Robotic Manipulators |