# Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information

arXiv: https://arxiv.org/abs/2307.14501 · HTML: https://mlsanity.com/paper/2307.14501/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 26 of 26)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
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- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [SOON: Scenario Oriented Object Navigation with Graph-based Exploration](https://mlsanity.com/paper/2103.17138/) (2021)
- [Learning to Navigate in Cities Without a Map](https://mlsanity.com/paper/1804.00168/) (2018)
- [Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation](https://mlsanity.com/paper/1703.00420/) (2017)
- [Unsupervised Predictive Memory in a Goal-Directed Agent](https://mlsanity.com/paper/1803.10760/) (2018)
- [Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments](https://mlsanity.com/paper/1612.05533/) (2016)
- [From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots](https://mlsanity.com/paper/1609.07910/) (2016)
- [Structured Object-Aware Physics Prediction for Video Modeling and Planning](https://mlsanity.com/paper/1910.02425/) (2019)
- [Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs](https://mlsanity.com/paper/2007.12640/) (2020)
- [Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning](https://mlsanity.com/paper/2004.11410/) (2020)
- [Semantic and Geometric Modeling with Neural Message Passing in 3D Scene Graphs for Hierarchical Mechanical Search](https://mlsanity.com/paper/2012.04060/) (2020)
- [Program Synthesis Guided Reinforcement Learning for Partially Observed Environments](https://mlsanity.com/paper/2102.11137/) (2021)
- [Representation, learning, and planning algorithms for geometric task and motion planning](https://mlsanity.com/paper/2203.04605/) (2022)
- [Learning and Planning for Temporally Extended Tasks in Unknown Environments](https://mlsanity.com/paper/2104.10636/) (2021)
- [Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty](https://mlsanity.com/paper/2105.04758/) (2021)
- [Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans](https://mlsanity.com/paper/2012.04735/) (2020)

## Abstract (arXiv)

We improve reliable, long-horizon, goal-directed navigation in partially-mapped environments by using non-locally available information to predict the goodness of temporally-extended actions that enter unseen space. Making predictions about where to navigate in general requires non-local information: any observations the robot has seen so far may provide information about the goodness of a particular direction of travel. Building on recent work in learning-augmented model-based planning under uncertainty, we present an approach that can both rely on non-local information to make predictions (via a graph neural network) and is reliable by design: it will always reach its goal, even when learning does not provide accurate predictions. We conduct experiments in three simulated environments in which non-local information is needed to perform well. In our large scale university building environment, generated from real-world floorplans to the scale, we demonstrate a 9.3% reduction in cost-to-go compared to a non-learned baseline and a 14.9% reduction compared to a learning-informed planner that can only use local information to inform its predictions.