Terrain Classification Enhanced with Uncertainty for Space Exploration Robots from Proprioceptive Data

Mariela De Lucas Alvarez, Jichen Guo, Raul Dominguez, Matias Valdenegro-Toro

Keywords: Deep Learning, Terrain classification, Space robotics, Uncertainty Quantification

Abstract: Terrain Classification is an essential task in space exploration, where unpredictable environments are difficult to observe using only exteroceptive sensors such as vision. Implementing Neural Network classifiers can have high performance but can be deemed untrustworthy as they lack transparency, which makes them unreliable for taking high-stakes decisions during mission planning. We address this by proposing Neural Networks with Uncertainty Quantification in Terrain Classification. We enable our Neural Networks with Monte Carlo Dropout, DropConnect, and Flipout in time series-capable architectures using only proprioceptive data as input. We use Bayesian Optimization with Hyperband for efficient hyperparameter optimization to find optimal models for trustworthy terrain classification.

Submission Type: Archival (to be published in the Journal of LatinX in AI (LXAI) Research)

Submission Number: 6