Dynamic Controllers in Character Animation
Transcript of Dynamic Controllers in Character Animation
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Dynamic Controllers in Character Animation
Jack Wang
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Overview
• Definition• Related Work• Composable Controllers Framework
(2001)• Results• Future Work
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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.
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Picture
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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
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Pose Controller
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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…
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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…
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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.
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Controllers in Animation
• Hand tuned controllers• Human athletics (running, vaulting,
cycling) – Hodgins et al., 1995• Human diving – Wooten and Hodgins,
1996• 3D bipedal walk – Laszlo et al., 1996
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Controllers in Animation
• Optimization• Simple planar figures - van de Panne and
Fiume, 1993• Virtual creatures – Sims, 1994• Aquatic animals - Grzeszczuk and
Terzopoulos, 1995
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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., 2000
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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.
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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., 2001
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Controller Abstraction
• Pre-Conditions• Just like in programming, regions in the state-
space 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.
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Example (Falling)
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Supervising Controller
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Typical Transitions
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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)
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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.
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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.
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Example
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Results
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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.
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Future Work
• Need controllers with human gaits.• Need model-independent algorithms
to synthesize controllers.• Need robust controllers. • Already 4 years into the future.
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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)
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(Partial) References• Composable Controllers for Physics-based Character
Animation, Faloutsos et al., SIGGRAPH 2001.• Composable Controllers for Physics-based Character
Animation, Petros Faloutsos, Phd. Thesis, University of Toronto.
• Dynamic Optimization of Human Walking, Anderson and Pandy, JBE, 2001.
• 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, 1998