Name
Abstract | ||
|---|---|---|
We introduce a particle filter-based approach to representing and actively reducing uncertainty over articulated motion models. The presented method provides a probabilistic model that integrates visual observations with feedback from manipulation actions to best characterize a distribution of possible articulation models. We evaluate several action selection methods to efficiently reduce the uncertainty about the articulation model. The full system is experimentally evaluated using a PR2 mobile manipulator. Our experiments demonstrate that the proposed system allows for intelligent reasoning about sparse, noisy data in a number of common manipulation scenarios. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICRA.2015.7139655 | IEEE International Conference on Robotics and Automation |
Keywords | Field | DocType |
manipulators,mobile robots,particle filtering (numerical methods),probability,uncertain systems,uncertainty handling,PR2 mobile manipulator,active articulation model estimation,articulated motion models,common manipulation scenarios,intelligent reasoning,interactive perception,manipulation actions,noisy data,particle filter-based approach,probabilistic model,uncertainty reduction,visual observations | Noisy data,Visualization,Particle filter,Statistical model,Artificial intelligence,Probabilistic logic,Engineering,Action selection,Perception,Machine learning,Mobile manipulator | Conference |
Volume | Issue | ISSN |
2015 | 1 | 1050-4729 |
Citations | PageRank | References |
12 | 0.58 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hausman, K. | 1 | 119 | 11.92 |
| S. Niekum | 2 | 165 | 23.73 |
| Sarah Osentoski | 3 | 323 | 21.47 |
| Gaurav S. Sukhatme | 4 | 5469 | 548.13 |