DegustaBot: Zero-Shot Visual Preference Estimation for Personalized Multi-Object Rearrangement

• URL: http://arxiv.org/abs/2407.08876v1
• Date: Thu, 11 Jul 2024 21:28:02 GMT
• Title: DegustaBot: Zero-Shot Visual Preference Estimation for Personalized Multi-Object Rearrangement
• Authors: Benjamin A. Newman, Pranay Gupta, Kris Kitani, Yonatan Bisk, Henny Admoni, Chris Paxton,
• Abstract summary: We present DegustaBot, an algorithm for visual preference learning that solves household multi-object rearrangement tasks according to personal preference. We collect a large dataset of naturalistic personal preferences in a simulated table-setting task. We find that 50% of our model's predictions are likely to be found acceptable by at least 20% of people.
• Score: 53.86523017756224
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: De gustibus non est disputandum ("there is no accounting for others' tastes") is a common Latin maxim describing how many solutions in life are determined by people's personal preferences. Many household tasks, in particular, can only be considered fully successful when they account for personal preferences such as the visual aesthetic of the scene. For example, setting a table could be optimized by arranging utensils according to traditional rules of Western table setting decorum, without considering the color, shape, or material of each object, but this may not be a completely satisfying solution for a given person. Toward this end, we present DegustaBot, an algorithm for visual preference learning that solves household multi-object rearrangement tasks according to personal preference. To do this, we use internet-scale pre-trained vision-and-language foundation models (VLMs) with novel zero-shot visual prompting techniques. To evaluate our method, we collect a large dataset of naturalistic personal preferences in a simulated table-setting task, and conduct a user study in order to develop two novel metrics for determining success based on personal preference. This is a challenging problem and we find that 50% of our model's predictions are likely to be found acceptable by at least 20% of people.

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