Garrett et al., 2020 - Google Patents
Online replanning in belief space for partially observable task and motion problemsGarrett et al., 2020
View PDF- Document ID
- 11742061359796976531
- Author
- Garrett C
- Paxton C
- Lozano-Pérez T
- Kaelbling L
- Fox D
- Publication year
- Publication venue
- 2020 IEEE International Conference on Robotics and Automation (ICRA)
External Links
Snippet
To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations. This may require opening a drawer to observe its contents or moving an object out of the way to examine the …
- 230000002708 enhancing 0 abstract description 5
Classifications
-
- 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
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
- G06N5/025—Extracting rules from data
-
- 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
- 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
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- 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 |
|---|---|---|
| Garrett et al. | Online replanning in belief space for partially observable task and motion problems | |
| Rybkin et al. | Model-based reinforcement learning via latent-space collocation | |
| Zhao et al. | A survey of optimization-based task and motion planning: From classical to learning approaches | |
| Brown et al. | Machine teaching for inverse reinforcement learning: Algorithms and applications | |
| Abel et al. | State abstractions for lifelong reinforcement learning | |
| Lozano-Pérez et al. | A constraint-based method for solving sequential manipulation planning problems | |
| Huang et al. | Continuous relaxation of symbolic planner for one-shot imitation learning | |
| He et al. | Reactive synthesis for finite tasks under resource constraints | |
| Ingrand et al. | Robotics and artificial intelligence: A perspective on deliberation functions | |
| Koralewski et al. | Self-specialization of general robot plans based on experience | |
| US20220146997A1 (en) | Device and method for training a control strategy with the aid of reinforcement learning | |
| Chen et al. | Predicting object interactions with behavior primitives: An application in stowing tasks | |
| Lee et al. | A model-based human activity recognition for human–robot collaboration | |
| Mahajan et al. | Robotic grasp detection by learning representation in a vector quantized manifold | |
| Lagrassa et al. | Learning skills to patch plans based on inaccurate models | |
| Gao et al. | Transferring hierarchical structures with dual meta imitation learning | |
| Ghalyan | Force-Controlled Robotic Assembly Processes of Rigid and Flexible Objects | |
| Mishra et al. | Generative factor chaining: Coordinated manipulation with diffusion-based factor graph | |
| Xue et al. | Logic-skill programming: An optimization-based approach to sequential skill planning | |
| Adu-Bredu et al. | Elephants don’t pack groceries: Robot task planning for low entropy belief states | |
| Feng et al. | Safety-constrained policy transfer with successor features | |
| Wang et al. | Temporal logic guided motion primitives for complex manipulation tasks with user preferences | |
| Elbarbari et al. | Ltlf-based reward shaping for reinforcement learning | |
| Urpí et al. | Efficient learning of high level plans from play | |
| Suárez-Hernández et al. | Practical resolution methods for mdps in robotics exemplified with disassembly planning |