PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments

Authors

  • Sung-Kyun Kim Jet Propulsion Laboratory
  • Amanda Bouman California Institute of Technology
  • Gautam Salhotra University of Southern California
  • David D. Fan Jet Propulsion Laboratory
  • Kyohei Otsu Jet Propulsion Laboratory
  • Joel Burdick California Institute of Technology
  • Ali-akbar Agha-mohammadi Jet Propulsion Laboratory

Keywords:

Planning With Uncertainty In Robotics, Real-world Robotic Planning Applications

Abstract

In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution. Making decisions for maximal reward in a stochastic setting requires value learning and policy construction over a belief space, i.e., probability distribution over all possible robot-world states. However, belief space planning in a large spatial environment over long temporal horizons suffers from severe computational challenges. Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime. This work proposes a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadMaps), that bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives. Leveraging hierarchical belief space planners with information-rich graph structures, PLGRIM addresses large-scale exploration problems while providing locally near-optimal coverage plans. We validate our proposed framework with high-fidelity dynamic simulations in diverse environments and on physical robots in Martian-analog lava tubes.

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Published

2021-05-17

How to Cite

Kim, S.-K., Bouman, A., Salhotra, G., Fan, D. D., Otsu, K., Burdick, J., & Agha-mohammadi, A.- akbar. (2021). PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments. Proceedings of the International Conference on Automated Planning and Scheduling, 31(1), 652-662. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/16014