Simulation of Human Motion using Model-Predictive Control

Marco da Silva, Yeuhi Abe and Jovan Popović

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.

Category:

Science & Technology

Tags:

• game
• development
• artifical
• intelligence
• animation

[toitotube]

Hola, me ha gustado mucho vuestro simulador.

Gracias.