Reliably Arranging Objects in Uncertain Domains
A crucial challenge in robotics is achieving reliable results in spite of sensing and control uncertainty. In this work, we explore the conformant planning approach to robot manipulation. In particular, we tackle the problem of pushing multiple planar objects simultaneously to achieve a specified ar...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2019
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| Online Access: | https://hdl.handle.net/1721.1/121463 |
| _version_ | 1826208738228305920 |
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| author | Anders, Ariel S Kaelbling, Leslie P Lozano-Perez, Tomas |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Anders, Ariel S Kaelbling, Leslie P Lozano-Perez, Tomas |
| author_sort | Anders, Ariel S |
| collection | MIT |
| description | A crucial challenge in robotics is achieving reliable results in spite of sensing and control uncertainty. In this work, we explore the conformant planning approach to robot manipulation. In particular, we tackle the problem of pushing multiple planar objects simultaneously to achieve a specified arrangement without external sensing. Conformant planning is a belief-state planning problem. A belief state is the set of all possible states of the world, and the goal is to find a sequence of actions that will bring an initial belief state to a goal belief state. To do forward belief-state planning, we created a deterministic belief-state transition model from supervised learning based on off-line physics simulations. We compare our method with an on-line physics-based manipulation approach and show significantly reduced planning times and increased robustness in simulated experiments. Finally, we demonstrate the success of this approach in simulations and physical robot experiments. |
| first_indexed | 2024-09-23T14:10:43Z |
| format | Article |
| id | mit-1721.1/121463 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:10:43Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1214632022-09-28T19:05:04Z Reliably Arranging Objects in Uncertain Domains Anders, Ariel S Kaelbling, Leslie P Lozano-Perez, Tomas Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science A crucial challenge in robotics is achieving reliable results in spite of sensing and control uncertainty. In this work, we explore the conformant planning approach to robot manipulation. In particular, we tackle the problem of pushing multiple planar objects simultaneously to achieve a specified arrangement without external sensing. Conformant planning is a belief-state planning problem. A belief state is the set of all possible states of the world, and the goal is to find a sequence of actions that will bring an initial belief state to a goal belief state. To do forward belief-state planning, we created a deterministic belief-state transition model from supervised learning based on off-line physics simulations. We compare our method with an on-line physics-based manipulation approach and show significantly reduced planning times and increased robustness in simulated experiments. Finally, we demonstrate the success of this approach in simulations and physical robot experiments. National Science Foundation (U.S.) (Grant 1420316) National Science Foundation (U.S.) (Grant 1523767) National Science Foundation (U.S.) (Grant 1723381) United States. Air Force. Office of Scientific Research (FA9550-17-1-0165) 2019-07-01T15:29:25Z 2019-07-01T15:29:25Z 2018-09-28 2018-05 2019-06-04T15:35:50Z Article http://purl.org/eprint/type/ConferencePaper 2577-087X https://hdl.handle.net/1721.1/121463 Anders, Ariel S., et al. “Reliably Arranging Objects in Uncertain Domains.” 2018 IEEE International Conference on Robotics and Automation (ICRA), 21-25 May, 2018, Brisbane, Queensland, Australia, IEEE, 2018, pp. 1603–10. en http://dx.doi.org/10.1109/ICRA.2018.8462892 2018 IEEE International Conference on Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Anders, Ariel S Kaelbling, Leslie P Lozano-Perez, Tomas Reliably Arranging Objects in Uncertain Domains |
| title | Reliably Arranging Objects in Uncertain Domains |
| title_full | Reliably Arranging Objects in Uncertain Domains |
| title_fullStr | Reliably Arranging Objects in Uncertain Domains |
| title_full_unstemmed | Reliably Arranging Objects in Uncertain Domains |
| title_short | Reliably Arranging Objects in Uncertain Domains |
| title_sort | reliably arranging objects in uncertain domains |
| url | https://hdl.handle.net/1721.1/121463 |
| work_keys_str_mv | AT andersariels reliablyarrangingobjectsinuncertaindomains AT kaelblinglesliep reliablyarrangingobjectsinuncertaindomains AT lozanopereztomas reliablyarrangingobjectsinuncertaindomains |