時空間検索に基づく事前知識を用いた運転指導の適応的計算モデル
Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving
運転などの複雑な運動技能の指導において、学習者の長期的な学習過程と指導者との繰り返しの相互作用を考慮した適応的指導モデルを提案。限られたデータでも、類似した過去の相互作用を検索して活用する手法で、非適応的なベースラインより優れた性能を示した。
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著者: Deepak Edakkattil Gopinath, Xiongyi Cui, Jonathan DeCastro, Avinash Balachandran, Guy Rosman
分類: cs.RO
原文アブストラクト
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.
