Universal Motion Generator: Trajectory Autocompletion by Motion Prompts
Name
Universal_Motion_Generator.pdf
Size
4.71 MB
Format
Adobe PDF
Checksum (MD5)
9b5b80996b367013bace9a4210970653
Author(s)
Wang, Yanwei
Shah, Julie
Date Issued
June 15, 2022
Abstract
Foundation models, which are large neural networks trained on massive datasets, have shown impressive generalization in both the language and the vision domain. While fine-tuning foundation models for new tasks at test-time is impractical due to billions of parameters in those models, prompts have been employed to re-purpose models for test-time tasks on the fly. In this report, we ideate the equivalent foundation model for motion generation and the corresponding formats of prompt that can condition such a model. The central goal is to learn a behavior prior for motion generation that can be re-used in a novel scene.
Subjects
Robot Learning, Large Language Models, Motion Generation
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
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