| dc.creator |
Wang, Yanwei |
|
| dc.creator |
Shah, Julie |
|
| dc.date |
2022-06-15T14:42:32Z |
|
| dc.date |
2022-06-15T14:42:32Z |
|
| dc.date |
2022-06-15 |
|
| dc.date.accessioned |
2023-02-17T20:00:21Z |
|
| dc.date.available |
2023-02-17T20:00:21Z |
|
| dc.identifier |
https://hdl.handle.net/1721.1/143430 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/242081 |
|
| dc.description |
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. |
|
| dc.description |
CSAIL NSF MI project – 6939398 |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.rights |
Attribution-NonCommercial-NoDerivs 3.0 United States |
|
| dc.rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/us/ |
|
| dc.subject |
Robot Learning, Large Language Models, Motion Generation |
|
| dc.title |
Universal Motion Generator: Trajectory Autocompletion by Motion Prompts |
|
| dc.type |
Working Paper |
|