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arXiv · 2606.25224

Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving

Deepak Edakkattil Gopinath·Xiongyi Cui·Jonathan DeCastro·Avinash Balachandran·Guy Rosman
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Abstract

Learning-based automated coaching systems for complex motor tasks such as high-performance driving remain limited in the ability to be adaptive by their reliance only on local, context-dependent reasoning, failing to account for the long-term temporal nature of student learning and the cumulative impact of repeated teacher-student interactions. In this paper, we propose an imitation learning based computational model for adaptive teaching with a dedicated temporal reasoning module that can reason over the interaction history under low-data regimes. To compensate for limited amounts of interactive training data, and based on the repetitive nature of the teaching process, the model relies on a nearest neighbor retrieval and cross attention prior, reasoning only on a narrowed-down set of semantically similar past interactions with an encoder-decoder based concurrent teaching model. We validate our approach with (i) a novel semi-synthetic closed-loop longitudinal student-teacher interaction dataset based on Waymo Open Motion Dataset and (ii) a small-scale real-world naturalistic simulator race coaching dataset. Our results reveal the consistent advantage of our adaptive teaching model with the nearest neighbor retrieval and cross-attention prior over a non-adaptive baseline as well as a suite of adaptive models that differ in their choice of priors and temporal fusion mechanisms.

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Deepak Edakkattil Gopinath, Xiongyi Cui, Jonathan DeCastro, Avinash Balachandran, Guy Rosman. 2026-06-23. Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving. https://arxiv.org/abs/2606.25224

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