Cognitive RoboticsRobot design:

Morphological computation

Soft robots

Whole body control

Dipartimento di Elettronica e Informazione

@ G. Gini 2015

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Morphological computation:connecting brain, body, and environment

Rolf Pfeifer

University of Zurich, Switzerland

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Two views of intelligence

by Shun Iwasawa

classical:“cognition as computation”

embodiment:“cognition emergent from sensory-motor and interaction processes”

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• “If properly applied, embodiment can lead to surprising insights. Although the idea has been around for quite some time, its implications for the design of autonomous adaptive systems have not yet been sufficiently explored and theoretically elaborated. As a consequence, robot designers often opt for centralized solutions where there is a microprocessor responsible for controlling the movement of all limbs and joints. Simply applying methods from control engineering to robots that have to perform in the real world has not worked well in practice”

• - Pfeifer

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Embodiment• trivial meaning:

“intelligence requires a body”• non-trivial meaning:

interplay brain (neural processing)morphology

• elasticity• stiffness• damping

materials• elastic• deformable

environment

“information theoretic”implications

Morphological computation

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• The effectsof motor commandsand sensoryfeedback depends on morphology

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“Understanding by building”

• modeling behavior of interest (movement, locomotion, sensory-motor coordination)

• abstracting principles

building robots for exploration of those principles

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Brain and sensors

relation to embodiment

• morphology performs part of the “computation” (pre-processing) -> fast, “free”

• dependence of learning speed on morphology

“morphological computation”

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morphology

“good” control- decentralized – no central resources required- “free” – exploitation of physical properties

• Control properties of muscle-tendon system

grasping of objectwinding a spring

effort, energy expenditurerelease

back to normal positionwithout control

• is exploited by brain

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locomotion

Passive Dynamic Walker: the brainless robotwalking without control

Cornell University

Morphology:- shape of feet- counterswing of arms- friction onbottom of feet

walking with little control

Morphology:- wide feet- elastic heels- counterswing of arms- friction on bottom offeet

--> high energy efficiency

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from locomotion to cognition

• grounding cognition in sensory-motor patterns• building a body image bottom-up• gait patterns

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Central pattern generator

• Central pattern generator (CPG) = neural net that generates rythmicactivities without using sensorial feedback (locomotion, respiration, …)

• 2 processes that interact: each sequentially increases and decreases, and the system returns to the starting condition

• Half-centered model (Brown 1914)

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environment

• constraints from morphologyand materials

• generation of correlations insensors through physicalprocess

–> good raw material forneural processing

(example: baby grasping object)

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sensory-motor coordination

• self-structuring of sensory data through – physical –interaction with environment

• reduction of complexity – induction of correlations• physical process – not „computational“

morphological computation

prerequisite for learning

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Complexity reduction through

sensory-motor coordinationsensory-motor coordination leads to• induction of correlations in different sensory

channels• dimensionality reduction (sensory data)→ information theoretic reason for sensory-motor

coordination→ basis for learning

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Active perception

“We begin not with a sensory stimulus, but with a sensory-motor coordination […] In a certain sense it is the movement which is primary, and the sensation which is secondary, the movement of the body, head, and eye muscles determining the quality of what is experienced. In other words, the real beginning is with the act of seeing; it is looking, and not a sensation of light.” (“The reflex arc in psychology,” John Dewey, 1896)

“Problems that are ill-posed, nonlinear, or unstable for a passive observer become well-posed, linear, or stable for an active observer.” (Ruzena Bajcsy, 1988)

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Learning and development

• dependence of sensory stimulation on behavior: sensory-motor coordination “good” raw material for neural system

• exploration dependent on constraints from morphology and materials structure in sensory data

Tononi, G., Sporns, O., and Edelman, G.M. (1996). A complexity measurefor selective matching of signals by the brain. Proc. Nat. Academy of Science (USA), 93, 3422-3427.

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Cognition from Bottom-up

• Categorization, perception, but even memory processes turn out tobe directly coupled to sensory-motor processes and thus toembodiment

• Rather than starting from representations of objects or the world, wepropose to start representing the very basis: the agent's body and itslow-level interaction with the environment.

• any cognitive processing will always be mediated by the body and the sensory-motor loops. Therefore, these are the first candidates for anagent to learn about.

•• Edelman, G. E. (1987), Neural Darwinism. The theory of neuronal

group selection., New York: Basic Books.Pfeifer, R. & Scheier, C. (1999), Understanding Intelligence, MIT Press.

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Summary morphological

• intelligence not (exclusively) in the brain:“morphological computation”

• task distribution betweenmorphology, materials, control (brain), and environment

or between“brain, body, and environment”

• sensory-motor coordination: induction of information structure

• role of embodiment in creating information structure major rationale for embodiment

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Nature as a model for robotics engineering

Glider(Alsomitra macrocarpa)

Aerodynamic dispersion of seeds (Courtesy of Wayne's Word)

Octopus adaptive shape, texture and camouflage

Courtesy of Roger T. Hanlon, Marine Biological Laboratory, Woods Hole, MA

Tumbleweed

Helicopter(Tipuana tipu)

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Six legged robot at Univ. of Michigan

Snake-like – by Mark Tilden

3D Bipedal Walking Dinosaur Robot at MIT

Biologically inspired robots

AIBO - Sony 2nd Generation ERS-210

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Applications of biomimetic robots

Mattel’s Miracle Moves Baby doll making realistic behavior of a baby.

Multi-limbed robots LEMUR (Limbed Excursion Mobile Utility Robot) at JPL.

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Technologies for morphological

• Soft robots• The bottleneck of actuation• Haptics

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• Muscles – result of evolution

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Rapid biomimetic prototyping reality

a.

b.

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Comparison between actuators

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Example: the hand

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EAPBiologically-Inspired Intelligent Robots using EAP as Biomimetic Actuation Materials

Yoseph Bar-Cohen, JPL/Caltech, Pasadena, CA

WW-EAP Webhub: http://eap.jpl.nasa.gov/

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Elements of an EAP actuated robots

Power

EAPActuator

Propulsion/Mobility/Locomotion Functions

Swimming and/or divingWalkingHopping and/or flyingMicroswitching and positioning

SensingEAP actuation sensorsImagingOther sensors as needed

Communication

Intelligent controlNavigationCollision avoidanceAutonomous performance

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Electroactive Polymers (EAP)

ELECTRONIC EAP• Dielectric EAP • Electrostrictive Graft Elastomers• Electrostrictive Paper• Electro-Viscoelastic Elastomers• Ferroelectric Polymers• Liquid Crystal Elastomers (LCE)

IONIC EAP• Carbon Nanotubes (CNT)• Conductive Polymers (CP)• ElectroRheological Fluids (ERF)• Ionic Polymer Gels (IPG)• Ionic Polymer Metallic Composite (IPMC)

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Electronic EAP

ELECTRIC FIELD OR COULOMB FORCES DRIVEN ACTUATORS

Ferroelectric [Q. Zhang, Penn State U.]

Graft Elastomer[J. Su, NASA LaRC]

Liquid crystals (Piezoelectric and thermo-mechanic)

[B. R. Ratna, NRL]

Voltage Off Voltage On

Dielectric EAP[R. Kornbluh, et al., SRI International]

Paper EAP [J. Kim, Inha University, Korea]

Temperature (C)40 50 60 70 80 90 100 110 120 130

Strain (%)

-30

-25

-20

-15

-10

-5

0

5 HeatingCooling

Applied tensile stress: 8kPaHeating/cooling rate: 0.5oC/min

MAOC4/MACC5 (50/50 mole%) with 10mole% of hexanediol diacrylate crosslinker

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Ionic EAPion change

IPMC[JPL using ONRI, Japan & UNM

materials] ElectroRheological Fluids (ERF)[ER Fluids Developments Ltd]

Ionic Gel[T. Hirai, Shinshu University, Japan]

Carbon-Nanotubes[R. Baughman et al, Honeywell, et al]

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Current EAP

Advantages and disadvantages

Except for CPs, ionic EAPs do not hold strain under DC voltageSlow response (fraction of a second)Bending EAPs induce a relatively low actuation forceExcept for CPs, it is difficult to produce a consistent material (particularly IPMC)

Large bending displacementsProvides mostly bending actuation (longitudinal mechanisms can be constructed)Requires low voltage

Ionic EAP

Requires high voltages (~150V/μm)Requires compromise between strain and stressinadequate for low temperature actuation tasks

Can operate in room conditions for a long timeRapid response (mSec levels)Can hold strain under DC activationInduces relatively large actuation forces

Electronic EAP

DisadvantagesAdvantagesEAP type

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Applications• Mechanisms

– Lenses with controlled configuration– Mechanical Lock– Noise reduction– Flight control surfaces/Jet flow control– Anti G-Suit

• Robotics, Toys and AnimatronicsBiologically-inspired RobotsToys and Animatronics

• Human-Machine InterfacesHaptic interfacesTactile interfacesOrientation indicator Smart flight/diving SuitsArtificial NoseBraille display (for Blind Persons)

• Planetary Applications– Sensor cleaner/wiper– Shape control of structures

• Medical Applications– EAP for Biological Muscle

Augmentation or Replacement– Miniature in-Vivo EAP Robots for

Diagnostics and Microsurgery– Catheter Steering Mechanism– Tissues Growth Engineering– Interfacing Neuron to Electronic

Devices Using EAP– Active Bandage

• Liquid and Gases Flow Control

• Controlled Weaving– Garment and Clothing

• MEMS

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MEMICA (MEchanical MIrroring using Controlled stiffness and Actuators)

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MEMICA-based exoskeleton for countermeasure of astronauts bones and muscles loss in microgravity. It has potential application as:

• Assist patient rehabilitation

• Enhance human mobility

NASA robonaut

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Human-Machine Interfaces

• Interfacing human and machine to complement or substitute our senses

Researchers at Duke U. connected electrodes to a brain of a money and were able to control a robotic arm. This breakthrough opens the possibility that the human brain would be able to operate prosthetics that are driven by EAP. Feedback is required to “feel” the environment around the artificial limbs. Currently, researchers are developing tactile sensors, haptic devices, and other interfaces.

Tactile Interface (S. Tadokoro, Kobe U., Japan)

Active Braille Display

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The grand challenge for EAP asArtificial Muscles

Robotic hand platform for EAP[G. Whiteley, Sheffield Hallam U., UK]

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Computational chemistry

Computational chemistry may lead to material design tools using comprehensive modeling to methodically synthesize effective new EAPs

(NASA-LaRC)

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Bio-hybrid actuators?

Work in progress

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measure finger positions compare with the virtual obj. (collision detection); if the hand-avatar is inside, apply a force

Haptic Interface

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Integrating haptic vision

Compare position and virtualobject-If contact then apply force on finger

Show virtual objectand contact

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Robots today

• What technologis are ready for the next generation ofrobots?

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The role of competitions

• DARPA robotics race challenge

2005 – first result forautonomus driving2016? First autonomus car? First autonomousflying car

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The role of competitions

• 2015 – first challenge for humanoid robots

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DARPA robotic challenge 2015

• drivetask• the robot begins in the vehicle, drives through the course,

and crosses the finish line. Teams are allowed five minuteswith no tools to modify the vehicle. The robot will begin the run in the vehicle, with the key in the ignition, the vehicleturned on and running, and the vehicle in “high” gear . Todrive the vehicle, the robot needs to depress the acceleratorand to rotate the steering wheel. The driving section of the course consists of a set of orange and white pylons thatblock the straight part with additional barriers defining the boundaries of the course.

• egresstask• the robot gets out of the vehicle, and travels to the end

zone. The robot may exit the vehicle from either side.

• doortask• the robot must open the door and then travel through the

open doorway. The DRC Finals will use a 36 inch doorwaywith a lever-style handle that operates either by rotatingdownward or by rotating upward.

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• valvetask• one valve with a circular handle with a diameter between 4

inches (10 cm) and 16 inches (40 cm). The valve opens bycounter-clockwise rotation. The task is considered compete when the robot has rotated the valve handle 360 degrees.

• walltask• the robot will use a cordless drill to cut through wall boards to

remove a prescribed shape. The wall material will be ½ inchthick drywall. There will be no obstruction or supports directlybehind the cut pattern.

• surprisetask• The Surprise task will require manipulation and no mobility (except to

get to and from the task site). The actual item will be disclosed to the teams the day before their run. The task may change each day.

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• rubbletask• the robot will earn one point for successfully traversing either

the debris field or the terrain field. Visible lines will mark the boundary between terrain and The blocks will not be fastenedto the ground. Terrain may shift during a run. For the Debrisside of the task, the robot begins behind the start line, so thatthe debris lies directly between the start point and the finish. A team needs to get to the other side by either movingthe debris or getting over it. The debris pieces will beconstructed of lightweight components, all less than 5 lbs.

• stairstask• the robot may only ascend, and may not descend. The

stairway has a rail on the left side and no rail on the right side.

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A competing robot• ATLAS - Biped from Boston Dynamics

• Full Body Control• CMU - a generalized

controller that works for a variety of tasks.two stages: low level and

high level controllers

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Uses desired motions from the

high level controller and estimated robot

states to compute target acceleration

Uses desired motions from high level controller and internal states to compute target velocityIntegrate to get position

The controller

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Atlas static walking

Foot step selection is entirely manual based on a live camera stream. Ankle is purely torque controlled

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Atlas ladder –step climbing

Foot / hand reposition is scripted, but final adjustment is done by the operator.

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Results?On Saturday evening, with their humanoid robot

DRC-Hubo, a team from the Korean AdvancedInstitute of Science and Technology (KAIST) in Daejeon, South Korea, won $2m from the R&Darm of the US defense department, Darpa, byoutperforming 24 other robots in a simulatednuclear reactor.

2° place - IHMC withATLAS (Google)3° place - Pennsylvania with CHIMP (Amazon)

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next

• Few developments in autonomy• Most human-robot interaction

Humanoids fromBoston Dynamics Japan

both funded by Google

The winner is a design from 2002Legs - wheels

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Robo ethics

• Military robots• - direct human intervention• - DoD scale• - define the process• - negotiate responsìbilities

• Medical robots• - FDA approval• - EBM

• Automatic cars• - a control tower?• - ad hoc roads?

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Nothing can stop automation