Learned Sampling Distributions for Efficient Planning in Hybrid Geometric and Object-Level Representations
Name
liu_stadler_icra_2020_v8.pdf
Description
Accepted version
Size
1.26 MB
Format
Adobe PDF
Checksum (MD5)
1c6a1b258f763ccba485f82fa5daa682
Author(s)
Liu, Katherine
Stadler, Martina
Roy, Nicholas
Date Issued
September 2020
Journal
Proceedings - IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Liu, Katherine, Stadler, Martina and Roy, Nicholas. 2020. "Learned Sampling Distributions for Efficient Planning in Hybrid Geometric and Object-Level Representations." Proceedings - IEEE International Conference on Robotics and Automation.
Version
Author's final manuscript
Abstract
© 2020 IEEE. We would like to enable a robotic agent to quickly and intelligently find promising trajectories through structured, unknown environments. Many approaches to navigation in unknown environments are limited to considering geometric information only, which leads to myopic behavior. In this work, we show that learning a sampling distribution that incorporates both geometric information and explicit, object-level semantics for sampling-based planners enables efficient planning at longer horizons in partially-known environments. We demonstrate that our learned planner is up to 2.7 times more likely to find a plan than the baseline, and can result in up to a 16% reduction in traversal costs as calculated by linear regression. We also show promising qualitative results on real-world data.
MIT Department
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/ICRA40945.2020.9196771
