Title:
Learning to Reach into the Unknown: Selecting Initial Conditions When Reaching in Clutter
Learning to Reach into the Unknown: Selecting Initial Conditions When Reaching in Clutter
| dc.contributor.author | Park, Daehyung | |
| dc.contributor.author | Kapusta, Ariel | |
| dc.contributor.author | Kim, You Keun | |
| dc.contributor.author | Rehg, James M. | |
| dc.contributor.author | Kemp, Charles C. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Institute for Robotics and Intelligent Machines | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Healthcare Robotics Lab | en_US |
| dc.date.accessioned | 2015-05-04T18:13:53Z | |
| dc.date.available | 2015-05-04T18:13:53Z | |
| dc.date.issued | 2014-09 | |
| dc.description | ©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
| dc.description | Presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 14-18 September 2014, Chicago, IL. | |
| dc.description | DOI: 10.1109/IROS.2014.6942625 | |
| dc.description.abstract | Often in highly-cluttered environments, a robot can observe the exterior of the environment with ease, but cannot directly view nor easily infer its detailed internal structure (e.g., dense foliage or a full refrigerator shelf). We present a data-driven approach that greatly improves a robot’s success at reaching to a goal location in the unknown interior of an environment based on observable external properties, such as the category of the clutter and the locations of openings into the clutter (i.e., apertures). We focus on the problem of selecting a good initial configuration for a manipulator when reaching with a greedy controller. We use density estimation to model the probability of a successful reach given an initial condition and then perform constrained optimization to find an initial condition with the highest estimated probability of success. We evaluate our approach with two simulated robots reaching in clutter, and provide a demonstration with a real PR2 robot reaching to locations through random apertures. In our evaluations, our approach significantly outperformed two alter- native approaches when making two consecutive reach attempts to goals in distinct categories of unknown clutter. Notably, our approach only uses sparse readily-apparent features. | en_US |
| dc.embargo.terms | null | en_US |
| dc.identifier.citation | Park, D.; Kapusta, A.; Kim, Y.K.; Rehg, J.M.; & Kemp, C.C. (2014). "Learning to Reach into the Unknown: Selecting Initial Conditions When Reaching in Clutter". IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 14-18 September, pp. 630-637. | en_US |
| dc.identifier.doi | 10.1109/IROS.2014.6942625 | |
| dc.identifier.uri | http://hdl.handle.net/1853/53330 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | |
| dc.subject | Apertures | en_US |
| dc.subject | Constrained optimization | en_US |
| dc.subject | Density estimation | en_US |
| dc.subject | Greedy controller | en_US |
| dc.title | Learning to Reach into the Unknown: Selecting Initial Conditions When Reaching in Clutter | en_US |
| dc.type | Text | |
| dc.type.genre | Post-print | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.author | Rehg, James M. | |
| local.contributor.author | Kemp, Charles C. | |
| local.contributor.corporatename | Healthcare Robotics Lab | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| local.contributor.corporatename | Rehabilitation Engineering Research Center on Technologies to Support Aging-in-Place for People with Long-Term Disabilities | |
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