Learned Sampling Distributions for Efficient Planning in Hybrid Geometric and Object-Level Representations
| Autor: | Katherine E. Liu, Nicholas Roy, Martina Stadler |
|---|---|
| Přispěvatelé: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Sampling (statistics) 02 engineering and technology Machine learning computer.software_genre Reduction (complexity) Tree traversal 020901 industrial engineering & automation Sampling distribution Linear regression 0202 electrical engineering electronic engineering information engineering Trajectory Robot 020201 artificial intelligence & image processing Artificial intelligence business Baseline (configuration management) computer |
| Zdroj: | MIT web domain ICRA |
| Popis: | © 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. |
| Databáze: | OpenAIRE |
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