CS 551Computer Animation

Lecture 15Lecture 15

Evolved Virtual CreaturesEvolved Virtual Creatures

Lecture 15Lecture 15

Evolved Virtual CreaturesEvolved Virtual Creatures

For next week

Video Textures: Schodl et al.Video Textures: Schodl et al.

Retargeting Motion to New Characters: GleicherRetargeting Motion to New Characters: Gleicher

Video Textures: Schodl et al.Video Textures: Schodl et al.

Retargeting Motion to New Characters: GleicherRetargeting Motion to New Characters: Gleicher

Karl Sims

Panspermia (1990)Panspermia (1990)

Particle Dreams (1988)Particle Dreams (1988)

Panspermia (1990)Panspermia (1990)

Particle Dreams (1988)Particle Dreams (1988)

Karl Sims

Evolved Virtual Creatures (SIGGRAPH 1994)Evolved Virtual Creatures (SIGGRAPH 1994)

Evolving 3D Morphology and Behavior by Evolving 3D Morphology and Behavior by Competition (ALIFECompetition (ALIFE1994)1994)

Evolved Virtual Creatures (SIGGRAPH 1994)Evolved Virtual Creatures (SIGGRAPH 1994)

Evolving 3D Morphology and Behavior by Evolving 3D Morphology and Behavior by Competition (ALIFECompetition (ALIFE1994)1994)

MotivationMotivation

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

• MorphologyMorphology

• ControlControl

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

• MorphologyMorphology

• ControlControl

Creating animation from scratch

Typical ScenarioTypical Scenario

• 20 seconds of animation20 seconds of animation

• 17 controlled DOFs17 controlled DOFs

• (20 * 30 * 17) = 10,200 torque values to determine(20 * 30 * 17) = 10,200 torque values to determine

• No approximations to best answer are knownNo approximations to best answer are known

– Spacetime constraints would use initial guess to find local Spacetime constraints would use initial guess to find local maximummaximum

Typical ScenarioTypical Scenario

• 20 seconds of animation20 seconds of animation

• 17 controlled DOFs17 controlled DOFs

• (20 * 30 * 17) = 10,200 torque values to determine(20 * 30 * 17) = 10,200 torque values to determine

• No approximations to best answer are knownNo approximations to best answer are known

– Spacetime constraints would use initial guess to find local Spacetime constraints would use initial guess to find local maximummaximum

Creating animation from scratch

Searching for 10,2000 unknownsSearching for 10,2000 unknowns

• We cannot prescribe initial values with confidenceWe cannot prescribe initial values with confidence

• We can prescribe some behaviors and principles we think We can prescribe some behaviors and principles we think the motion will demonstratethe motion will demonstrate

– The movements at some DOFs are functions of The movements at some DOFs are functions of movements at other DOFsmovements at other DOFs

Sun and Metaxas – Automating Sun and Metaxas – Automating Gait Generation, SIGGRAPH 01Gait Generation, SIGGRAPH 01

Searching for 10,2000 unknownsSearching for 10,2000 unknowns

• We cannot prescribe initial values with confidenceWe cannot prescribe initial values with confidence

• We can prescribe some behaviors and principles we think We can prescribe some behaviors and principles we think the motion will demonstratethe motion will demonstrate

– The movements at some DOFs are functions of The movements at some DOFs are functions of movements at other DOFsmovements at other DOFs

Sun and Metaxas – Automating Sun and Metaxas – Automating Gait Generation, SIGGRAPH 01Gait Generation, SIGGRAPH 01

So we’re not really working from scratch

Searching for 10,200 unknownsSearching for 10,200 unknowns

• How to incorporate our motion heuristics into search?How to incorporate our motion heuristics into search?

– Simulated annealing?Simulated annealing?

– Neural networks?Neural networks?

– Spacetime constraints?Spacetime constraints?

– Genetic algorithms?Genetic algorithms?

Searching for 10,200 unknownsSearching for 10,200 unknowns

• How to incorporate our motion heuristics into search?How to incorporate our motion heuristics into search?

– Simulated annealing?Simulated annealing?

– Neural networks?Neural networks?

– Spacetime constraints?Spacetime constraints?

– Genetic algorithms?Genetic algorithms?

But we don’t know the morphology

The previous example assumed a known The previous example assumed a known morphologymorphology

• We’re searching for best architecture AND best control We’re searching for best architecture AND best control simultaneously!simultaneously!

Our search space just grew to an unknown degree!Our search space just grew to an unknown degree!

The previous example assumed a known The previous example assumed a known morphologymorphology

• We’re searching for best architecture AND best control We’re searching for best architecture AND best control simultaneously!simultaneously!

Our search space just grew to an unknown degree!Our search space just grew to an unknown degree!

Imitate the processes of evolution and natural Imitate the processes of evolution and natural selection in biological world to generate/evolve selection in biological world to generate/evolve the best solutionthe best solution

Imitate the processes of evolution and natural Imitate the processes of evolution and natural selection in biological world to generate/evolve selection in biological world to generate/evolve the best solutionthe best solution

Genetic Algorithms

(not to scale)

Genetic Algorithms

Inject user knowledgeInject user knowledge

• Objective function Objective function

• Genetic representationGenetic representation

– Let’s search for collection of jointsLet’s search for collection of joints

– Let’s not search for n * 30 * 20 unknown joint torquesLet’s not search for n * 30 * 20 unknown joint torques

– Instead, search for n equations that are logical combinations of Instead, search for n equations that are logical combinations of virtual sensors of creature statevirtual sensors of creature state

– These equations are reused for all timestepsThese equations are reused for all timesteps

• Genetic operatorsGenetic operators

– Recombination and mutationRecombination and mutation

Inject user knowledgeInject user knowledge

• Objective function Objective function

• Genetic representationGenetic representation

– Let’s search for collection of jointsLet’s search for collection of joints

– Let’s not search for n * 30 * 20 unknown joint torquesLet’s not search for n * 30 * 20 unknown joint torques

– Instead, search for n equations that are logical combinations of Instead, search for n equations that are logical combinations of virtual sensors of creature statevirtual sensors of creature state

– These equations are reused for all timestepsThese equations are reused for all timesteps

• Genetic operatorsGenetic operators

– Recombination and mutationRecombination and mutation

Sensibility of GA for this problem

Motion principles described as heuristicsMotion principles described as heuristics• Minimize angular disturbances by swinging legs in opposite Minimize angular disturbances by swinging legs in opposite

directionsdirections

• Maximize balance by keeping center of mass above platform Maximize balance by keeping center of mass above platform of supportof support

• Kinematics of arm are similar to kinematics of legKinematics of arm are similar to kinematics of leg

So let’s find those heuristics and permit them to So let’s find those heuristics and permit them to be shared across limbsbe shared across limbs

Motion principles described as heuristicsMotion principles described as heuristics• Minimize angular disturbances by swinging legs in opposite Minimize angular disturbances by swinging legs in opposite

directionsdirections

• Maximize balance by keeping center of mass above platform Maximize balance by keeping center of mass above platform of supportof support

• Kinematics of arm are similar to kinematics of legKinematics of arm are similar to kinematics of leg

So let’s find those heuristics and permit them to So let’s find those heuristics and permit them to be shared across limbsbe shared across limbs

Related work

Ngo and Marks – Spacetime Constraints Revisited Ngo and Marks – Spacetime Constraints Revisited (SIGGRAPH 88)(SIGGRAPH 88)

van de Panne and Fiume – Sensor-actuator van de Panne and Fiume – Sensor-actuator Networks (SIGGRAPH 93)Networks (SIGGRAPH 93)

Dr. P’s L-system trees –Dr. P’s L-system trees – published everywhere published everywhere

Ngo and Marks – Spacetime Constraints Revisited Ngo and Marks – Spacetime Constraints Revisited (SIGGRAPH 88)(SIGGRAPH 88)

van de Panne and Fiume – Sensor-actuator van de Panne and Fiume – Sensor-actuator Networks (SIGGRAPH 93)Networks (SIGGRAPH 93)

Dr. P’s L-system trees –Dr. P’s L-system trees – published everywhere 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

• [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

Create a new populationCreate a new population

• [Selection][Selection] Select two parents solutions according to their Select two parents solutions according to their fitnessfitness

• [Recombination][Recombination] Combine parents to form offspring Combine parents to form offspring

• [Mutation][Mutation] Potentially new offspring at each position Potentially new offspring at each position

Create a new populationCreate a new population

• [Selection][Selection] Select two parents solutions according to their Select two parents solutions according to their fitnessfitness

• [Recombination][Recombination] Combine parents to form offspring Combine parents to form offspring

• [Mutation][Mutation] Potentially new offspring at each position Potentially new offspring at each position

Genetic Algorithms

Application of Genetic Algorithm

• Objective function Objective function

• Genetic representationGenetic representation

• Genetic operatorsGenetic operators

• Objective function Objective function

• Genetic representationGenetic representation

• Genetic operatorsGenetic operators

Objective Function

• To create realistic looking and acting To create realistic looking and acting creaturescreatures

• Fitness functions used to help evaluate if Fitness functions used to help evaluate if the objective function is satisfiedthe objective function is satisfied

• To create realistic looking and acting To create realistic looking and acting creaturescreatures

• Fitness functions used to help evaluate if Fitness functions used to help evaluate if the objective function is satisfiedthe objective function is satisfied

Genetic Representation

Defined by creature morphology and neurologyDefined by creature morphology and neurology

Morphology:Morphology: “The form and structure of an “The form and structure of an organism or one of its parts”organism or one of its parts”

Neurology Neurology (Nervous system): “The system of (Nervous system): “The system of cells, tissues, and organs that regulates the cells, tissues, and organs that regulates the body's responses to internal and external body's responses to internal and external stimuli”stimuli”

Defined by creature morphology and neurologyDefined by creature morphology and neurology

Morphology:Morphology: “The form and structure of an “The form and structure of an organism or one of its parts”organism or one of its parts”

Neurology Neurology (Nervous system): “The system of (Nervous system): “The system of cells, tissues, and organs that regulates the cells, tissues, and organs that regulates the body's responses to internal and external body's responses to internal and external stimuli”stimuli”

Creature MorphologyCreature Morphology

Articulated three-dimension Articulated three-dimension rigid body partsrigid body parts

Represented as a directed Represented as a directed graph of nodes and graph of nodes and connectionsconnections

Each node describes one Each node describes one body partbody part

• Many parameters to a nodeMany parameters to a node

Articulated three-dimension Articulated three-dimension rigid body partsrigid body parts

Represented as a directed Represented as a directed graph of nodes and graph of nodes and connectionsconnections

Each node describes one Each node describes one body partbody part

• Many parameters to a nodeMany parameters to a node

Creature Neurology

Comprised of:Comprised of:

• Sensors Sensors

• NeuronsNeurons

• EffectorsEffectors

Comprised of:Comprised of:

• Sensors Sensors

• NeuronsNeurons

• EffectorsEffectors

SensorsSensors

Each sensor is at a different part of the bodyEach sensor is at a different part of the body

MeasureMeasure

• That body partThat body part

• The world relative to that body partThe world relative to that body part

Different types of sensorsDifferent types of sensors

• Joint angle sensorJoint angle sensor

• Contact sensorsContact sensors

• Photo sensorsPhoto sensors

Each sensor is at a different part of the bodyEach sensor is at a different part of the body

MeasureMeasure

• That body partThat body part

• The world relative to that body partThe world relative to that body part

Different types of sensorsDifferent types of sensors

• Joint angle sensorJoint angle sensor

• Contact sensorsContact sensors

• Photo sensorsPhoto sensors

NeuronsNeurons

Virtual brain: Function based on sensor input Virtual brain: Function based on sensor input

Provides output effector valuesProvides output effector values

Virtual brain: Function based on sensor input Virtual brain: Function based on sensor input

Provides output effector valuesProvides output effector values

EffectorsEffectors

Each effector controls a degree of freedom in Each effector controls a degree of freedom in a jointa joint

Each effector’s value is applied as forces or Each effector’s value is applied as forces or torquestorques

Only exert simulated muscle forceOnly exert simulated muscle force

Connected to neuron or sensorConnected to neuron or sensor

Each effector controls a degree of freedom in Each effector controls a degree of freedom in a jointa joint

Each effector’s value is applied as forces or Each effector’s value is applied as forces or torquestorques

Only exert simulated muscle forceOnly exert simulated muscle force

Connected to neuron or sensorConnected to neuron or sensor

Neural SystemNeural System

Local neural system:Local neural system:

• Neural system generated along with morphological systemNeural system generated along with morphological system

• Connection allowed between local neural systemsConnection allowed between local neural systems

Global neural system:Global neural system:

• Neurons not associated with a specific nodeNeurons not associated with a specific node

– Useful heuristic for creating global timing mechanismsUseful heuristic for creating global timing mechanisms

• Can be connected with local neural systemsCan be connected with local neural systems

Local neural system:Local neural system:

• Neural system generated along with morphological systemNeural system generated along with morphological system

• Connection allowed between local neural systemsConnection allowed between local neural systems

Global neural system:Global neural system:

• Neurons not associated with a specific nodeNeurons not associated with a specific node

– Useful heuristic for creating global timing mechanismsUseful heuristic for creating global timing mechanisms

• Can be connected with local neural systemsCan be connected with local neural systems

Neural System

• Sum, product, divide, threshold, Sum, product, divide, threshold, >, sign, min, abs, if, interpolate, >, sign, min, abs, if, interpolate, sine, cosine, atan, log, exponent, sine, cosine, atan, log, exponent, sigmoid, integrate, differentiate, sigmoid, integrate, differentiate, smooth, memory, wave, sawtoothsmooth, memory, wave, sawtooth

• Sum, product, divide, threshold, Sum, product, divide, threshold, >, sign, min, abs, if, interpolate, >, sign, min, abs, if, interpolate, sine, cosine, atan, log, exponent, sine, cosine, atan, log, exponent, sigmoid, integrate, differentiate, sigmoid, integrate, differentiate, smooth, memory, wave, sawtoothsmooth, memory, wave, sawtooth

Physical Simulation

• Featherstone’s articulated body methodFeatherstone’s articulated body method

• Runge-Kutta-FehlbergRunge-Kutta-Fehlberg

– Adaptive timestep typically 1-5 steps per 1/30 secondAdaptive timestep typically 1-5 steps per 1/30 second

• Bounding boxes to reduce collision checksBounding boxes to reduce collision checks

– Connected pairs can interpenetrate but cannot rotate Connected pairs can interpenetrate but cannot rotate entirely through one anotherentirely through one another

• Impulses and springs used for collision correctionsImpulses and springs used for collision corrections

• Viscosity used for underwaterViscosity used for underwater

• Featherstone’s articulated body methodFeatherstone’s articulated body method

• Runge-Kutta-FehlbergRunge-Kutta-Fehlberg

– Adaptive timestep typically 1-5 steps per 1/30 secondAdaptive timestep typically 1-5 steps per 1/30 second

• Bounding boxes to reduce collision checksBounding boxes to reduce collision checks

– Connected pairs can interpenetrate but cannot rotate Connected pairs can interpenetrate but cannot rotate entirely through one anotherentirely through one another

• Impulses and springs used for collision correctionsImpulses and springs used for collision corrections

• Viscosity used for underwaterViscosity used for underwater

Genetic Operators: Mutation Alter internal node parameters

Dimensions- determine the physical shape of the partDimensions- determine the physical shape of the part

Joint type- number of DOF for each joint and movement Joint type- number of DOF for each joint and movement allowed for each DOFallowed for each DOF

Joint limits- point where spring forces will be exerted for Joint limits- point where spring forces will be exerted for each DOFeach DOF

Recursive limitRecursive limit

Set of local neuronsSet of local neurons

Set of connections to other nodesSet of connections to other nodes• Position, orientation, scale and reflection relative to parent Position, orientation, scale and reflection relative to parent

Dimensions- determine the physical shape of the partDimensions- determine the physical shape of the part

Joint type- number of DOF for each joint and movement Joint type- number of DOF for each joint and movement allowed for each DOFallowed for each DOF

Joint limits- point where spring forces will be exerted for Joint limits- point where spring forces will be exerted for each DOFeach DOF

Recursive limitRecursive limit

Set of local neuronsSet of local neurons

Set of connections to other nodesSet of connections to other nodes• Position, orientation, scale and reflection relative to parent Position, orientation, scale and reflection relative to parent

Genetic Operators: Mutation Add a new node at random

Leg Segment

BodySegment

Head Segment

Genetic Operators: MutationParameter of each connection can change

Leg Segment

BodySegment

9045

Genetic Operators: MutationAdd/remove connections

Leg Segment

BodySegment

Head Segment

Genetic Operators: Mutation Delete unconnected nodes

Leg Segment

BodySegment

Head Segment

Genetic Operators: Crossover

Genetic Operators: Grafting

StartStart

Generate random population of Generate random population of nn

suitable solutions for the problemsuitable solutions for the problem • Random generation of nodes and connectionsRandom generation of nodes and connections

• Use existing genotype to seed functionUse existing genotype to seed function

• Create genotype manually to seed functionCreate genotype manually to seed function

Generate random population of Generate random population of nn

suitable solutions for the problemsuitable solutions for the problem • Random generation of nodes and connectionsRandom generation of nodes and connections

• Use existing genotype to seed functionUse existing genotype to seed function

• Create genotype manually to seed functionCreate genotype manually to seed function

Fitness

Swimming, walking, jumping Swimming, walking, jumping • Distance traveled by COM/unit of timeDistance traveled by COM/unit of time

FollowingFollowing• Speed at which creature moves towards targetSpeed at which creature moves towards target

CompetitionCompetition• Pairs of individuals compete for control over a cubePairs of individuals compete for control over a cube• Final distance of the creature from cube and opponents Final distance of the creature from cube and opponents

final distance from cubefinal distance from cube

Swimming, walking, jumping Swimming, walking, jumping • Distance traveled by COM/unit of timeDistance traveled by COM/unit of time

FollowingFollowing• Speed at which creature moves towards targetSpeed at which creature moves towards target

CompetitionCompetition• Pairs of individuals compete for control over a cubePairs of individuals compete for control over a cube• Final distance of the creature from cube and opponents Final distance of the creature from cube and opponents

final distance from cubefinal distance from cube

New population

• Survival ratio: 1/5 of 300Survival ratio: 1/5 of 300

• 40% mutation, 30% crossover, 30% grafting40% mutation, 30% crossover, 30% grafting

• Children from crossover and grafting are Children from crossover and grafting are sometimes mutatedsometimes mutated

• Survival ratio: 1/5 of 300Survival ratio: 1/5 of 300

• 40% mutation, 30% crossover, 30% grafting40% mutation, 30% crossover, 30% grafting

• Children from crossover and grafting are Children from crossover and grafting are sometimes mutatedsometimes mutated

ResultsResults

Different runs converged on characters with Different runs converged on characters with different evolved characteristicsdifferent evolved characteristics

Specify a User Preference using the AlgorithmSpecify a User Preference using the Algorithm

• User can specify a preference over multiple runs of the User can specify a preference over multiple runs of the simulationsimulation

• User can perform the natural selectionUser can perform the natural selection

Different runs converged on characters with Different runs converged on characters with different evolved characteristicsdifferent evolved characteristics

Specify a User Preference using the AlgorithmSpecify a User Preference using the Algorithm

• User can specify a preference over multiple runs of the User can specify a preference over multiple runs of the simulationsimulation

• User can perform the natural selectionUser can perform the natural selection

Results

VideoVideoVideoVideo

AdvantagesAdvantages

Generate characters without specification or Generate characters without specification or knowledge about exactly how they workknowledge about exactly how they work

Less likely to get stuck on a local solution Less likely to get stuck on a local solution since we are searching on an encodingsince we are searching on an encoding

Easy to parallelizeEasy to parallelize

Generate characters without specification or Generate characters without specification or knowledge about exactly how they workknowledge about exactly how they work

Less likely to get stuck on a local solution Less likely to get stuck on a local solution since we are searching on an encodingsince we are searching on an encoding

Easy to parallelizeEasy to parallelize

DisadvantagesDisadvantages

The characters could produce incorrect behavior that The characters could produce incorrect behavior that could go undetected because of the lack of could go undetected because of the lack of understanding of the underlying algorithmunderstanding of the underlying algorithm

Process is not very reusableProcess is not very reusable

• Can reuse some of the components of the implementation, for Can reuse some of the components of the implementation, for example, the neuron languageexample, the neuron language

Genetic Algorithms sometimes require a lot of Genetic Algorithms sometimes require a lot of tweaking to work righttweaking to work right

The characters could produce incorrect behavior that The characters could produce incorrect behavior that could go undetected because of the lack of could go undetected because of the lack of understanding of the underlying algorithmunderstanding of the underlying algorithm

Process is not very reusableProcess is not very reusable

• Can reuse some of the components of the implementation, for Can reuse some of the components of the implementation, for example, the neuron languageexample, the neuron language

Genetic Algorithms sometimes require a lot of Genetic Algorithms sometimes require a lot of tweaking to work righttweaking to work right

DisadvantagesDisadvantages

Difficult to obtain ideal size for the neuron language, Difficult to obtain ideal size for the neuron language, node types and parameters (joint types and node types and parameters (joint types and parameters and connection parameters), sensor parameters and connection parameters), sensor and effector types and effector types

i.e. Difficult to obtain best size for the search spacei.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 small, then there aren't enough variations in the

populationpopulation

• If the size is too large, then changes in isolated parts of 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 structurecreature do not have a big effect on the behavior or structure

Difficult to obtain ideal size for the neuron language, Difficult to obtain ideal size for the neuron language, node types and parameters (joint types and node types and parameters (joint types and parameters and connection parameters), sensor parameters and connection parameters), sensor and effector types and effector types

i.e. Difficult to obtain best size for the search spacei.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 small, then there aren't enough variations in the

populationpopulation

• If the size is too large, then changes in isolated parts of 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 structurecreature do not have a big effect on the behavior or structure

Additional References

http://www.genetic-programming.com/http://www.genetic-programming.com/

http://lancet.mit.edu/~mbwall/presentations/http://lancet.mit.edu/~mbwall/presentations/IntroToGAs/IntroToGAs/

http://cs.felk.cvut.cz/~xobitko/ga/http://cs.felk.cvut.cz/~xobitko/ga/

http://www.genetic-programming.com/http://www.genetic-programming.com/

http://lancet.mit.edu/~mbwall/presentations/http://lancet.mit.edu/~mbwall/presentations/IntroToGAs/IntroToGAs/

http://cs.felk.cvut.cz/~xobitko/ga/http://cs.felk.cvut.cz/~xobitko/ga/