# Risk-aware Meta-level Decision Making for Exploration Under Uncertainty

arXiv: https://arxiv.org/abs/2209.05580 · HTML: https://mlsanity.com/paper/2209.05580/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 16 of 16)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Online Meta-Learning](https://mlsanity.com/paper/1902.08438/) (2019)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge](https://mlsanity.com/paper/2103.11470/) (2021)
- [Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving](https://mlsanity.com/paper/2007.00178/) (2020)
- [Autonomous Spot: Long-Range Autonomous Exploration of Extreme Environments with Legged Locomotion](https://mlsanity.com/paper/2010.09259/) (2020)
- [Bayesian Optimized Monte Carlo Planning](https://mlsanity.com/paper/2010.03597/) (2020)
- [Deep Learning Tubes for Tube MPC](https://mlsanity.com/paper/2002.01587/) (2020)
- [PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments](https://mlsanity.com/paper/2102.05633/) (2021)
- [Learning Risk-aware Costmaps for Traversability in Challenging Environments](https://mlsanity.com/paper/2107.11722/) (2021)
- [FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget](https://mlsanity.com/paper/2203.06316/) (2022)
- [Bi-directional Value Learning for Risk-aware Planning Under Uncertainty: Extended Version](https://mlsanity.com/paper/1902.05698/) (2019)
- [Planning for robotic exploration based on forward simulation](https://mlsanity.com/paper/1502.02474/) (2015)
- [Learning When to Quit: Meta-Reasoning for Motion Planning](https://mlsanity.com/paper/2103.04374/) (2021)
- [A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions](https://mlsanity.com/paper/2104.15081/) (2021)

## Built on by (most cited first, 1 of 1)

- [Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2209.07660/) (2022)

## Abstract (arXiv)

Robotic exploration of unknown environments is fundamentally a problem of decision making under uncertainty where the robot must account for uncertainty in sensor measurements, localization, action execution, as well as many other factors. For large-scale exploration applications, autonomous systems must overcome the challenges of sequentially deciding which areas of the environment are valuable to explore while safely evaluating the risks associated with obstacles and hazardous terrain. In this work, we propose a risk-aware meta-level decision making framework to balance the tradeoffs associated with local and global exploration. Meta-level decision making builds upon classical hierarchical coverage planners by switching between local and global policies with the overall objective of selecting the policy that is most likely to maximize reward in a stochastic environment. We use information about the environment history, traversability risk, and kinodynamic constraints to reason about the probability of successful policy execution to switch between local and global policies. We have validated our solution in both simulation and on a variety of large-scale real world hardware tests. Our results show that by balancing local and global exploration we are able to significantly explore large-scale environments more efficiently.