Harvard University

The Harvard Hand – An Adaptive Gripper for Simple, Robust Grasping

Aaron M. Dollar and Robert D. Howe

Harvard UniversitySchool of Engineering and Applied Sciences

Harvard University

Research Question

• Can the problems associated with robotic grasping in the presence of uncertainty(unstructured environments) be addressed by careful mechanical design of robot hands?

Harvard University

Structured Environment

• Assembly line

www.msl.ri.cmu.edu

Harvard University

Structured Environment

• Assembly line

www.msl.ri.cmu.edu

Object properties and locationare well known

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Structured Environment

• Size and shape of the object are precisely known

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Structured Environment

• Specialized gripper• Simple task execution

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Unstructured Environment

• Messy Desk

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Unstructured Environment

• Messy Desk

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Unstructured Environment

• Object properties and location unknown– Difficult to find with sensing

Harvard University

Grasping in Unstructured Environments

• Traditional approach: Complex hands– Many DOFs– Lots of sensing

Utah/MIT handrobonaut.jsc.nasa.gov

Harvard University

Grasping in Unstructured Environments

• Complex hands = Complicated!– Difficult to control– Expensive– Fragile

Utah/MIT handrobonaut.jsc.nasa.gov

Harvard University

Grasping in Unstructured Environments

• Complex hands = Complicated!– Difficult to control– Expensive– Fragile

They don’t work reliablyUtah/MIT hand

robonaut.jsc.nasa.gov

Harvard University

Grasping in Unstructured Environments

• How to deal with “poor” sensing?– Errors in positioning,

finger placement– Can’t control contact forces

Grasp will likely be unsuccessfulUtah/MIT hand

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Our Approach

* “Smart” mechanical design for simplicity of use and robust operation

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Our Approach

• Make the hand

– Soft, flexible joints and fingerpads• Minimizes undesirable contact forces• Gripper passively conforms to objects

Compliant

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Our Approach

• Incorporate behavior

– More DOFs than actuators• “Underactuated”• Joints are coupled

– Passively adapts to object shape, location

– Simplifies hardware and control

Adaptive

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Our Approach

• construction

– Unstructured environment unplanned contact

– Withstand large forces without damage

Durable

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Our Approach

• Result:

– Easy to control– Reliable under uncertainty– Immune to harsh treatment

Simple + Robust

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Design Optimization Studies

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Design Optimization Studies

FabricationDurable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Design Optimization Studies

Fabrication

Evaluation

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Design Optimization Studies

Fabrication

Evaluation

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Our Approach

• Make the hand

– Soft, flexible joints and fingerpads• Minimizes undesirable contact forces• Gripper passively conforms to objects

How should the compliant hand be designed?

Compliant

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Optimization Goal

• Find the hand configuration that leads to largest Successful Grasp Space with minimum Contact Forces Grasp Space

Object

Contact Forces

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Optimization Goal

• Find the hand configuration that leads to largest Successful Grasp Space with minimum Contact Forces– Simulate the grasping process

• Vary joint angles and stiffness• Examine effect on performance

Grasp Space

Object

Contact Forces

kbase

kmiddle

φ1

φ2

Harvard University

Grasp Space

Object

Contact Forces

kbase

kmiddle

φ1

φ2

Simulation Result

Optimum joint rest angles: φ1,φ2=(25º,45º) Optimum joint stiffness: kbase<< kmiddle

– Optimum across wide range of object size

Harvard University

Our Approach

• Incorporate behavior

– More DOFs than actuators• “Underactuated”• Joints are coupled

– Passively adapts to object shape, location– Simplifies hardware and control

Adaptive

Harvard University

Underactuated/Adaptive Hands

• Other effective adaptive hands– Barrett Hand

• Most widely used “dexterous” robot hand

– 7 DOF, 4 actuators

– Laval University Hands• E.g. SARAH hand

– 10 DOF, 2 actuators

www.barretttechnology.com

wwwrobot.gmc.ulaval.ca

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Motivation

• How should joints be coupled for good grasping performance?

Harvard University

Motivation

• How should joints be coupled for good grasping performance?– Very little research in this area

• Kaneko et al. 2005 • Birglen and Gosselin 2004

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Design Optimization

Object

• Simple (simplest?) gripper– Two fingers– Two joints each – Springs in joints

• Planar approximation to a number of popular hands– Barrett, Domo, Laval, Obrero, SPRING

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Actuation Scheme

• Consider a tendon-driven finger– By varying pulley radii and

location of link idler, affect actuation behavior

– Results of study apply to other transmission methods

• One actuator per hand (4 joints)

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Actuation Optimization

• Built simulation– Vary joint torque ratio (distal:proximal)

• Tendon routing + joint stiffnesses determine joint torque ratio

– Find maximum Grasp Space, minimum Contact Forces

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Design Optimization

Object

RobotMotion

• Scenario– Object ≈ circle (planar)

• unmovable

Harvard University

Design Optimization

Object

RobotMotion

• Simple (simplest?) gripper– Two fingers– Two joints each – Springs in joints

• Planar approximation to a number of popular hands– Barrett, Domo, Laval, Obrero, SPRING

Harvard University

Joint Coupling Optimization

Object

RobotMotion

General scenario:1. Sense approximate object location

(e.g. vision)2. Move straight to object 3. Detect contact, stop robot4. Close gripper

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Grasp Scenario

Initial contact, no deflection

Begin actuation Finger 2 contact,force application Object enclosure

Harvard University

Actuation Scheme

• To enable analysis, analyzed tendon-driven finger– By varying pulley radii and

location of link idler, affect actuation behavior

– Results of study apply to other transmission methods

• One actuator per hand (4 joints)

Harvard University

Actuation Optimization

• Built simulation– Vary joint torque ratio (distal:proximal)

• Tendon routing + joint stiffnesses determine joint torque ratio

– Find maximum Grasp Space, minimum Contact Forces

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Contact Force

Large ObjectSmall Object

Object location(distance

from hand center)

Torque Ratio τmiddle/τbase

Grasp fails

Simulation Results

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Simulation Results

Tradeoff between low forces and large grasp range

Contact Force

Large ObjectSmall Object

Object location(distance

from hand center)

Torque Ratio τmiddle/τbase

Grasp fails

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Analysis of Results

• Consider the quality of sensory information– E.g. don’t need large grasp space when sensing is good

large torque ratio, low forces

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Analysis of Results

• Assume a normal distribution z of object position from expected position– Low σ for good sensing– High σ for poor sensing

Object2

2( )21( , , )

2

tx x

tz x x e σσσ π

− −

=

z

( , , ) ( )x

tp x x z x dxσ−∞

′ ′= ∫

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Small Object

Object location(distance

from hand center)

Torque Ratio τmiddle/τbase

Grasp fails

Simulation Results

• For good sensing, want to grasp off-center

Lowest forces

Object

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Analysis of Results

• xt is the off-center “target”position

2

2( )21( , , )

2

tx x

tz x x e σσσ π

− −

=

z

*Predicted object location

xt

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Weighted Force

• Weighted average over position and object radius– “Expectation” of force

• Forces near expected position weighted more strongly

max

max

( / )

0( / )

0

( / , ) ( , , )( / , , )

( , , )R

c r r

c r r

x k

Ru r r c c t cF u r r t x k

c t c

F k x z x x dxQ k x

z x x dx

τ

τ

τ στ σ

σ= ∫

∫

Harvard University

Weighted Grasp Space

• Probability object is within the successful grasp space of the hand

max max max( / , , , ) ( ( / ), , ) ( ( / ), , )cx r r c t c r r t c r r tQ k x x p x k x p x k xτ σ τ σ τ σ= − −

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Overall Quality Measure

• Quotient of the two quality measures

max ( / , , , )( / , , , )

( / , , , )c

Ru

x r r c tr r c t

F r r c t

Q k x xQ k x x

Q k x xτ σ

τ στ σ

=

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Overall Quality Measure

• Poor sensing– σ/l=1.5

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Overall Quality Measure

• Poor sensing– σ/l=1.5

Target at center (xt=0)Torque ratio of ~ 1:1

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Design Optimization Studies

Fabrication

Evaluation

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Design Optimization Studies

Fabrication

Evaluation

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Our Approach

• construction

– Unstructured environment unplanned contact– Withstand large forces without damage

Build a durable hand using the design principles from the optimization studies

Durable

Harvard University

Durable Robotics

• Rarely addressed in research robots• Essential in military, space, and industrial

applications– iRobot’s “Packbot”– Minnesota’s “Scouts”

www.irobot.com Minnesota’s scout

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Durable Robotics

• For research, durability opens doors:– Expands the types of tasks that can be attempted– Speeds implementation by reducing need for

careful validation of programs

Utah/MIT hand

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A Robust Grasper

• Fabrication process Durable construction– Large impact forces

• Compliant, adaptive joints Robust grasping– Large uncertainties in object location

Harvard University

Shape Deposition Manufacturing (SDM)

• Build up the part in layers

• Embed components– Protect fragile parts

• Dissimilar materials• No fasteners!

Courtesy of Mark Cutkosky

Harvard University

SDM robots

• Sprawl family of robots• RiSE robots

Courtesy of Mark Cutkosky Courtesy of Mark Cutkosky

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Tendon cable

Soft fingerpads

Viscoelastic flexure joints

Stiff links

Hollow cable raceway

Dovetail connector

2cm

Embedded cable anchor

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Mechanism Behavior

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Grasper Design

• Design based on previous optimization study

φ1=25°

φ2=45°l = 0.07m (2.75”)

l

k1k2≈5k1

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-0.2

0

0.2

0.4

0.6

0.8

0 0.2 0.4 0.6 0.8 1Join

t ang

le (r

ad)

Proximal jointDistal joint

• Joint material is highly viscoelastic– 2nd order kelvin model: 0.016 0.00130.18 0.03 0.04t tk e eθ

− −= + +

Time (sec)

– Damps out unwanted joint oscillations

Mechanism Behavior

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Grasper Prototype

• 4 fingers • 8 joints• 1 actuator

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Hand Overview

• Slightly larger than human hand– Sized for use in human

environments

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Tendon Actuation Scheme

• Equal tension on all fingers– Regardless of position, contact

• Adaptable!

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Tendon Actuation Scheme

• Tendons in parallel with compliance much stiffer when actuated– Soft during exploration, acquisition– Stiff, stable grasp

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Durability

Harvard University

Design Optimization Studies

FabricationDurable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Design Optimization Studies

Fabrication

Evaluation

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

Harvard University

Our Approach

• Result:

– Easy to control– Reliable under uncertainty– Immune to harsh treatment

Durable

Compliant

++Adaptive

++Simple + Robust

Harvard University

Hand Evaluation

• Experiment 1: – Measure Successful Grasp Space

• “Allowable error” in hand positioning

– Record Contact Forces• Low forces until stable grasp

Object

Contact Forces

Grasp Space

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Experimental Platform

• Hand mounted on WAM robot arm– 3 DOF– No wrist!

• No orientation control

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Experiment 1

• 2 objects– PVC tube (r =24mm)– Wood block (84mm

x 84mm)

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Experiment 1

• Grasp range results– PVC tube

• ±5cm in x – symmetric @ center

• +2cm, -3cm in y

~100% of object size

x

PVC Tube

y

Harvard University

Experiment 1

• Grasp range results– Wood block

• ±2cm in x – symmetric @ center

• ±2cm in y

~45% of object size

Woodblock

xy

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Experiment 2

• Autonomous grasping across workspace

• Guided by single image– Simple USB webcam

• 640x480 resolution

– Looking down on workspace

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Teleoperation

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Future Work

• Relax assumptions on optimization studies– 3D, non-circular objects, not “unmovable”

• Incorporate wrist on experimental platform– Further experimental studies

• Grasp quality measures under uncertainty• Investigate role of sensing

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Acknowledgement

This work was supported by the Office of Naval Research grant number N00014-98-1-0669.

Harvard University

Call for Papers

Robot Manipulation: Sensing and Adapting to the Real World

Workshop at Robotics: Science and Systems 2007Atlanta, GA, USA

• submission deadline - May 1st • notification of acceptance - May 15th • workshop - June 30th

Harvard University

Analysis

• Initial contact andbeginning Actuation

ii i

ikτψ ϕ= − for i=2,3,4

11

1

sincos

cx ra ϕϕ

+=

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Analysis

• Contact on link 3

3 1a a=

3 3 3sin cos 0cont cont cr a xψ ψ− − =

xc

φ1

k2

k1

ψ3cont

a1 a3

ψ4

ψ2

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Analysis

• Contact on outer links

12 4

1

2 tancont contr

l aψ ψ π − ⎛ ⎞

= = − ⎜ ⎟−⎝ ⎠

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Overall Quality Measure

• Good sensing– Average doesn’t make

sense– No predetermined xt

• Can target according to object size

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Overall Quality Measure

• Good sensing– Take maximum for

each torque ratio

Harvard University

Overall Quality Measure

• Good sensing– Take maximum for

each torque ratio

Optimum at ~ 1:1

Harvard University

Grasper Fabrication Process

magnets

connectors

Hallsensors

tendoncable

low-frictiontubes

Pockets with embedded componentsA CB

ED F

Dam materialStiff polymer

New pockets

Soft polymers Soft polymers

Stiff polymer Complete fingers

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Mechanism Behavior

• Very low tip stiffness– x=5.85 N/m– y=7.72 N/m– z=14.2 N/m

• Large displacements• Impact resistant!