SYNAPSE: 記号的神経支援による選好合成エンジン
SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine
視覚的なデモンストレーションからユーザー固有の選好（例：良い停車位置）を少数データで学習するため、視覚解析・大規模言語モデル・プログラム合成を組み合わせた神経記号的手法を提案。
著者: Sadanand Modak, Noah Patton, Isil Dillig, Joydeep Biswas
分類: cs.RO, cs.CV, cs.LG, cs.PL
原文アブストラクト
This paper addresses the problem of preference learning, which aims to align robot behaviors through learning user specific preferences (e.g. "good pull-over location") from visual demonstrations. Despite its similarity to learning factual concepts (e.g. "red door"), preference learning is a fundamentally harder problem due to its subjective nature and the paucity of person-specific training data. We address this problem using a novel framework called SYNAPSE, which is a neuro-symbolic approach designed to efficiently learn preferential concepts from limited data. SYNAPSE represents preferences as neuro-symbolic programs, facilitating inspection of individual parts for alignment, in a domain-specific language (DSL) that operates over images and leverages a novel combination of visual parsing, large language models, and program synthesis to learn programs representing individual preferences. We perform extensive evaluations on various preferential concepts as well as user case studies demonstrating its ability to align well with dissimilar user preferences. Our method significantly outperforms baselines, especially when it comes to out of distribution generalization. We show the importance of the design choices in the framework through multiple ablation studies. Code, additional results, and supplementary material can be found on the website: https://amrl.cs.utexas.edu/synapse
