CS Introduction to Computer Graphics

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					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

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
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
• We’re searching for best architecture AND best control

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
• 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
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
• [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
  • 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
Creature Morphology
Articulated three-dimension
 rigid body parts
Represented as a directed
 graph of nodes and
Each node describes one
 body part
  • Many parameters to a node
Creature Neurology
Comprised of:
  • Sensors
  • Neurons
  • Effectors

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

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

Each effector controls a degree of freedom in
 a joint
Each effector’s value is applied as forces or
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

Genetic Operators: Mutation
Parameter of each connection can change


Genetic Operators: Mutation
Add/remove connections

              Leg        Head
            Segment     Segment

Genetic Operators: Mutation
Delete unconnected nodes

              Leg        Head
            Segment     Segment

Genetic Operators: Crossover
Genetic Operators: Grafting

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
Swimming, walking, jumping
  • Distance traveled by COM/unit of time
  • Speed at which creature moves towards target
  • 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

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
  • User can perform the natural selection

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
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
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
   • 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