Dynamic Controllers in Character Animation. Jack Wang

Transcription

1 Dynamic Controllers in Character Animation Jack Wang

2 Overview Definition Related Work Composable Controllers Framework (2001) Results Future Work 2

3 Physics-based Animation Dynamic Controllers vs. Simulation Active characters. Dynamic Controllers vs. Optimal Trajectory Physics constraints are enforced to a different degree. Animator constraints are enforced to a different degree. 3

4 Picture 4

5 Example Controllers Open loop, no feed back, does not depend on current state. Close loop, output is a function of the current state. torque = K_s(q q_des) K_dq is a Proportional Derivative (PD) controller Neural-Network type controller (including Sensor-Actuator Networks) Pose controller 5

6 Pose Controller 6

7 Related Fields Biomechanics Highly detailed models, geared towards applications in medicine. Anderson and Pandy, 2001 Solves for the muscle activation history of half a walk cycle. Minimizes metabolic energy per unit distance traveled. 810 dimensional optimization problem. Etc 7

8 Related Fields Motor Neuroscience Interested in how the brain issues motor commands. Harris and Wolpert, 1998 Studied goal-directed arm and eye movements. Control signals are corrupted by multiplicative noise. Proposes people minimize variance in final position when planning trajectory. Etc 8

9 Related Fields Robotics Significant overlap with controller-based animation in terms of interests, especially in humanoid and animal-like robots. Large amount of work done in locomotion controllers. State of the art Honda robot still doesn t walk like humans do. 9

10 Controllers in Animation Hand tuned controllers Human athletics (running, vaulting, cycling) Hodgins et al., 1995 Human diving Wooten and Hodgins, D bipedal walk Laszlo et al.,

11 Controllers in Animation Optimization Simple planar figures - van de Panne and Fiume, 1993 Virtual creatures Sims, 1994 Aquatic animals - Grzeszczuk and Terzopoulos,

12 Controllers in Animation Motion capture modification Tracking upper body movements Zordan and Hodgins, 1999 Interactive animation Driving planar characters with mouse input Laszlo et al.,

13 Synthesis Methods Design by hand Optimization Could design an initial controller by hand. Gradient usually unavailable. Reinforcement Learning Algorithms have been developed to perform optimization in stochastic environments. 13

14 Composable Controllers People have been synthesizing controllers to perform specific tasks. A framework to combine controllers so that more complex tasks can be performed, Faloutsos et al.,

15 Controller Abstraction Pre-Conditions Just like in programming, regions in the statespace that the controller can operate. Unlike in programming, success is not guaranteed. Post-Conditions Defines what success means. Expected Performance Regions in state-space that are likely for the controller to succeed, once execution has started. 15

16 Example (Falling) 16

17 Supervising Controller 17

18 Typical Transitions 18

19 More on Pre-Conditions Can be hard to set manually. Can be formulated as a classification problem: given initial state and controller, classify success and failure. Use Support Vector Machines (SVM) 19

20 Linear Support Vector Machines Solves for a hyperplane in state-space that maximizes the distance to the closest data points (support vectors), constrained by the separation of data. 20

21 Nonlinear Boundary Map data points to higher, possibly infinite dimensional space, where they could be separated by a linear boundary. Introduce kernel functions. Basically, they are inner products of data in a higher dimensional space. 21

22 Example 22

23 Results 23

24 Observations Very robotic motion. No locomotion capabilities to the 3D character. Best results come from highly dynamic plunging, falling motion. Controllers cannot be easily adapted to new models. 24

25 Future Work Need controllers with human gaits. Need model-independent algorithms to synthesize controllers. Need robust controllers. Already 4 years into the future. 25

26 Possible Directions Synthesize controllers under motor noise Necessary for robotics, more robust controllers. Surprisingly good effect on gait. (Lawrence et al., 2003) Passive Dynamics Simplify control by engineering models that can naturally perform unstable tasks. (Collins et al., 2005) 26

27 (Partial) References Composable Controllers for Physics-based Character Animation, Faloutsos et al., SIGGRAPH Composable Controllers for Physics-based Character Animation, Petros Faloutsos, Phd. Thesis, University of Toronto. Dynamic Optimization of Human Walking, Anderson and Pandy, JBE, Signal-dependent noise determines motor planning, Harris and Wolpert, Nature, 1998 Efficient Gradient Estimation for Motor Control Learning, Lawrence et al., UAI, 2003 Efficient Bipedal Robots Based on Passive-Dynamic Walkers, Collins et al., Nature, 2005 A Tutorial on Supper Vector Machines for Pattern Recognition. DMKD,