期刊
2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
卷 -, 期 -, 页码 9555-9562出版社
IEEE
DOI: 10.1109/icra40945.2020.9196771
关键词
-
资金
- Army Research Laboratory [W911NF-17-2-0181]
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.
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