Epistemic Uncertainty in State Estimation and Belief Space Planning with Learning-Based Perception Systems
Keywords: learned perception, belief space planning, out of distribution data
Abstract: Learning-based models for robot perception are known to suffer from two distinct sources of error: aleatoric and epistemic. Aleatoric uncertainty arises from inherently noisy training data and is easily quantified from residual errors during training. Conversely, epistemic uncertainty arises from a lack of training data, appearing in out-of-distribution operating regimes, and is difficult to quantify. In this work, we propose: (i) an epistemic Kalman filter (EpiKF) to incorporate epistemic uncertainty into state estimation with learned perception models, and (ii) an epistemic belief space planner (EpiBSP) that builds on the EpiKF to plan trajectories to avoid areas of high epistemic and aleatoric uncertainty. Our key insight is to train a generative model that predicts measurements from states, ``inverting" the learned perception model that predicts states from measurements.
Submission Number: 13
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