Universal Motion Generator: Trajectory Autocompletion by Motion Prompts
Author(s)Wang, Yanwei; Shah, Julie
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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.
Robot Learning, Large Language Models, Motion Generation
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