CS Introduction to Computer Graphics

Document Sample

```					CS 551
Computer Animation
Lecture 15
Evolved Virtual Creatures
For next week
Video Textures: Schodl et al.

Retargeting Motion to New Characters: Gleicher
Karl Sims
Panspermia (1990)

Particle Dreams (1988)
Karl Sims
Evolved Virtual Creatures (SIGGRAPH 1994)

Evolving 3D Morphology and Behavior by
Competition (ALIFE
1994)
Motivation

Automatically generate physically simulated
characters without fully understanding the
procedures used to build and control them

• Morphology
• Control
Creating animation from scratch
Typical Scenario
• 20 seconds of animation
• 17 controlled DOFs
• (20 * 30 * 17) = 10,200 torque values to determine
• No approximations to best answer are known
– Spacetime constraints would use initial guess to find local
maximum
Creating animation from scratch
Searching for 10,2000 unknowns
• We cannot prescribe initial values with confidence
• We can prescribe some behaviors and principles we think the
motion will demonstrate
– The movements at some DOFs are functions of
movements at other DOFs
 Sun and Metaxas – Automating
Gait Generation, SIGGRAPH 01
So we’re not really working from scratch
Searching for 10,200 unknowns
• How to incorporate our motion heuristics into search?
– Simulated annealing?
– Neural networks?
– Spacetime constraints?
– Genetic algorithms?
But we don’t know the morphology
The previous example assumed a known
morphology
• We’re searching for best architecture AND best control
simultaneously!

Our search space just grew to an unknown degree!
Genetic Algorithms
Imitate the processes of evolution and natural
selection in biological world to generate/evolve
the best solution

(not to scale)
Genetic Algorithms
Inject user knowledge
• Objective function
• Genetic representation
– Let’s search for collection of joints
– Let’s not search for n * 30 * 20 unknown joint torques
– Instead, search for n equations that are logical combinations of
virtual sensors of creature state
– These equations are reused for all timesteps
• Genetic operators
– Recombination and mutation
Sensibility of GA for this problem
Motion principles described as heuristics
• Minimize angular disturbances by swinging legs in opposite
directions
• Maximize balance by keeping center of mass above platform
of support
• Kinematics of arm are similar to kinematics of leg

So let’s find those heuristics and permit them to
be shared across limbs
Related work
Ngo and Marks – Spacetime Constraints Revisited
(SIGGRAPH 88)
van de Panne and Fiume – Sensor-actuator
Networks (SIGGRAPH 93)
Dr. P’s L-system trees –
published everywhere

Juan Buhler
Genetic Algorithms
• [Start] Generate random population of n suitable
solutions for the problem
• [Fitness] Evaluate the fitness of each solution in
the population by running physical simulation
• [New population] Create a new population
• [Replace] Replace current population with newly
generated population
• [Test] If the end condition is satisfied, stop, and
return the best
Genetic Algorithms
Create a new population
• [Selection] Select two parents solutions according to their
fitness
• [Recombination] Combine parents to form offspring
• [Mutation] Potentially new offspring at each position
Application of Genetic Algorithm

• Objective function
• Genetic representation
• Genetic operators
Objective Function

• To create realistic looking and acting
creatures
• Fitness functions used to help evaluate if
the objective function is satisfied
Genetic Representation
Defined by creature morphology and neurology
Morphology: “The form and structure of an
organism or one of its parts”
Neurology (Nervous system): “The system of
cells, tissues, and organs that regulates the
body's responses to internal and external
stimuli”
Creature Morphology
Articulated three-dimension
rigid body parts
Represented as a directed
graph of nodes and
connections
Each node describes one
body part
• Many parameters to a node
Creature Neurology
Comprised of:
• Sensors
• Neurons
• Effectors
Sensors

Each sensor is at a different part of the body
Measure
• That body part
• The world relative to that body part
Different types of sensors
• Joint angle sensor
• Contact sensors
• Photo sensors
Neurons

Virtual brain: Function based on sensor input
Provides output effector values
Effectors

Each effector controls a degree of freedom in
a joint
Each effector’s value is applied as forces or
torques
Only exert simulated muscle force
Connected to neuron or sensor
Neural System
Local neural system:
• Neural system generated along with morphological system
• Connection allowed between local neural systems

Global neural system:
• Neurons not associated with a specific node
– Useful heuristic for creating global timing mechanisms
• Can be connected with local neural systems
Neural System
• Sum, product, divide, threshold,
>, sign, min, abs, if, interpolate,
sine, cosine, atan, log, exponent,
sigmoid, integrate, differentiate,
smooth, memory, wave, sawtooth
Physical Simulation
• Featherstone’s articulated body method
• Runge-Kutta-Fehlberg
– Adaptive timestep typically 1-5 steps per 1/30 second
• Bounding boxes to reduce collision checks
– Connected pairs can interpenetrate but cannot rotate
entirely through one another
• Impulses and springs used for collision corrections
• Viscosity used for underwater
Genetic Operators: Mutation
Alter internal node parameters
Dimensions- determine the physical shape of the part
Joint type- number of DOF for each joint and movement
allowed for each DOF
Joint limits- point where spring forces will be exerted for
each DOF
Recursive limit
Set of local neurons
Set of connections to other nodes
• Position, orientation, scale and reflection relative to parent
Genetic Operators: Mutation
Add a new node at random

Leg        Head
Segment     Segment

Body
Segment
Genetic Operators: Mutation
Parameter of each connection can change

90
45
Leg
Segment

Body
Segment
Genetic Operators: Mutation
Add/remove connections

Leg        Head
Segment     Segment

Body
Segment
Genetic Operators: Mutation
Delete unconnected nodes

Leg        Head
Segment     Segment

Body
Segment
Genetic Operators: Crossover
Genetic Operators: Grafting
Start

Generate random population of n
suitable solutions for the problem
• Random generation of nodes and connections
• Use existing genotype to seed function
• Create genotype manually to seed function
Fitness
Swimming, walking, jumping
• Distance traveled by COM/unit of time
Following
• Speed at which creature moves towards target
Competition
• Pairs of individuals compete for control over a cube
• Final distance of the creature from cube and opponents
final distance from cube
New population
•   Survival ratio: 1/5 of 300
•   40% mutation, 30% crossover, 30% grafting
•   Children from crossover and grafting are
sometimes mutated
Results

Different runs converged on characters with
different evolved characteristics
Specify a User Preference using the Algorithm
• User can specify a preference over multiple runs of the
simulation
• User can perform the natural selection
Results
Video
Advantages

Generate characters without specification or
knowledge about exactly how they work
Less likely to get stuck on a local solution
since we are searching on an encoding
Easy to parallelize
Disadvantages
The characters could produce incorrect behavior that
could go undetected because of the lack of
understanding of the underlying algorithm
Process is not very reusable
• Can reuse some of the components of the implementation, for
example, the neuron language

Genetic Algorithms sometimes require a lot of
tweaking to work right
Disadvantages
Difficult to obtain ideal size for the neuron language,
node types and parameters (joint types and
parameters and connection parameters), sensor
and effector types
i.e. Difficult to obtain best size for the search space
• If the size is too small, then there aren't enough variations in the
population
• If the size is too large, then changes in isolated parts of the
creature do not have a big effect on the behavior or structure
Additional References
http://www.genetic-programming.com/
http://lancet.mit.edu/~mbwall/presentations/IntroT
oGAs/
http://cs.felk.cvut.cz/~xobitko/ga/

```
DOCUMENT INFO
Shared By:
Categories:
Stats:
 views: 3 posted: 6/9/2011 language: English pages: 42