Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics

• URL: http://arxiv.org/abs/2009.10191v1
• Date: Mon, 21 Sep 2020 21:56:44 GMT
• Title: Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics
• Authors: S. Banerjee, J. Harrison, P. M. Furlong, M. Pavone
• Abstract summary: Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. High level terrain classification is not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap. Online rover-terrain interaction data can be leveraged to accurately predict future dynamics and prevent further damage to the rover.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rovers require knowledge of terrain to plan trajectories that maximize safety and efficiency. Terrain type classification relies on input from human operators or machine learning-based image classification algorithms. However, high level terrain classification is typically not sufficient to prevent incidents such as rovers becoming unexpectedly stuck in a sand trap; in these situations, online rover-terrain interaction data can be leveraged to accurately predict future dynamics and prevent further damage to the rover. This paper presents a meta-learning-based approach to adapt probabilistic predictions of rover dynamics by augmenting a nominal model affine in parameters with a Bayesian regression algorithm (P-ALPaCA). A regularization scheme is introduced to encourage orthogonality of nominal and learned features, leading to interpretable probabilistic estimates of terrain parameters in varying terrain conditions.

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