Simulation of human motion data using short-horizon model-predictive control
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2008
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| Online Access: | http://hdl.handle.net/1721.1/43041 |
| _version_ | 1826200002841542656 |
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| author | Silva, Marco Jorge Tome da |
| author2 | Jovan Popović. |
| author_facet | Jovan Popović. Silva, Marco Jorge Tome da |
| author_sort | Silva, Marco Jorge Tome da |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. |
| first_indexed | 2024-09-23T11:29:11Z |
| format | Thesis |
| id | mit-1721.1/43041 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:29:11Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/430412019-04-11T09:21:51Z Simulation of human motion data using short-horizon model-predictive control Silva, Marco Jorge Tome da Jovan Popović. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. Includes bibliographical references (p. 52-56). Many data-driven animation techniques are capable of producing high quality motions of human characters. Few techniques, however, are capable of generating motions that are consistent with physically simulated environments. Physically simulated characters, in contrast, are automatically consistent with the environment, but their motions are often unnatural because they are difficult to control. We present a model-predictive controller that yields natural motions by guiding simulated humans toward real motion data. During simulation, the predictive component of the controller solves a quadratic program to compute the forces for a short window of time into the future. These forces are then applied by a low-gain proportional-derivative component, which makes minor adjustments until the next planning cycle. The controller is fast enough for interactive systems such as games and training simulations. It requires no precomputation and little manual tuning. The controller is resilient to mismatches between the character dynamics and the input motion, which allows it to track motion capture data even where the real dynamics are not known precisely. The same principled formulation can generate natural walks, runs, and jumps in a number of different physically simulated surroundings. by Marco da Silva. S.M. 2008-11-07T18:55:17Z 2008-11-07T18:55:17Z 2008 2008 Thesis http://hdl.handle.net/1721.1/43041 243776990 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 56 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Silva, Marco Jorge Tome da Simulation of human motion data using short-horizon model-predictive control |
| title | Simulation of human motion data using short-horizon model-predictive control |
| title_full | Simulation of human motion data using short-horizon model-predictive control |
| title_fullStr | Simulation of human motion data using short-horizon model-predictive control |
| title_full_unstemmed | Simulation of human motion data using short-horizon model-predictive control |
| title_short | Simulation of human motion data using short-horizon model-predictive control |
| title_sort | simulation of human motion data using short horizon model predictive control |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/43041 |
| work_keys_str_mv | AT silvamarcojorgetomeda simulationofhumanmotiondatausingshorthorizonmodelpredictivecontrol |