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Computer Science > Robotics
Title: Synapse: Learning Preferential Concepts from Visual Demonstrations
(Submitted on 25 Mar 2024 (this version), latest version 7 May 2024 (v2))
Abstract: This paper addresses the problem of preference learning, which aims to learn user-specific preferences (e.g., "good parking spot", "convenient drop-off location") from visual input. Despite its similarity to learning factual concepts (e.g., "red cube"), 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 new framework called Synapse, which is a neuro-symbolic approach designed to efficiently learn preferential concepts from limited demonstrations. Synapse represents preferences as neuro-symbolic programs 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 evaluate Synapse through extensive experimentation including a user case study focusing on mobility-related concepts in mobile robotics and autonomous driving. Our evaluation demonstrates that Synapse significantly outperforms existing baselines as well as its own ablations. The code and other details can be found on the project website this https URL .
Submission history
From: Sadanand Modak [view email][v1] Mon, 25 Mar 2024 12:23:39 GMT (8513kb,D)
[v2] Tue, 7 May 2024 02:47:55 GMT (8695kb,D)
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