We introduce a novel multilabel and multimodal transfer learning technique for modeling olfactory perception. Our approach aims to tackle the challenges of data scarcity and label skewness in the olfactory domain.

We study how an offline dataset of prior (possibly random) experience can be used to address challenges that autonomous systems face when they endeavor to learn from, adapt to, and collaborate with humans. First, we use the offline dataset to efficiently infer the human's reward function via pool-based active preference learning. Second, given this learned reward function, we perform offline reinforcement learning to optimize a policy based on the inferred human intent.

Geometric methods for solving open-world off-road navigation tasks, by learning occupancy and metric maps, provide good generalization but can be brittle in outdoor environments. Learning-based methods can directly learn collision-free behavior from raw observations, but are difficult to integrate with standard geometry based pipelines. This creates an unfortunate conflict – either use learning and lose out on well-understood geometric navigational components, or do not use it, in favor of extensively handtuned geometry-based cost maps. In this work, we reject this dichotomy by designing the learning and non-learning-based components in a way such that they can be effectively combined in a self-supervised manner.