Efficient Planning for Near-Optimal Compliant Manipulation Leveraging Environmental Contact
Name
guan2018efficient.pdf
Description
Accepted version
Size
1.11 MB
Format
Adobe PDF
Checksum (MD5)
15492a8a4375397578da1d93cc8f2b5f
Author(s)
Guan, Charlie
Vega-Brown, William R
Roy, Nicholas
Date Issued
September 2018
Journal
IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Guan, Charlie et al. "Efficient Planning for Near-Optimal Compliant Manipulation Leveraging Environmental Contact." May 2018, IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, Institute of Electrical and Electronics Engineers (IEEE), September 2018 © 2018 IEEE
Version
Author's final manuscript
Abstract
Path planning classically focuses on avoiding environmental contact. However, some assembly tasks permit contact through compliance, and such contact may allow for more efficient and reliable solutions under action uncertainty. But, optimal manipulation plans that leverage environmental contact are difficult to compute. Environmental contact produces complex kinematics that create difficulties for planning. This complexity is usually addressed by discretization over state and action space, but discretization quickly becomes computationally intractable. To overcome the challenge, we use the insight that only actions on configurations near the contact manifold are likely to involve complex kinematics, while segments of the plan through free space do not. Leveraging this structure can greatly reduce the number of states considered and scales much better with problem complexity. We develop an algorithm based on this idea and show that it performs comparably to full MDP solutions at a fraction of the computational cost.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8462696
