Harvard University

Simple, Robust Grasping in

Unstructured Environments

Aaron Dollar1 and Robert D. Howe2

1Massachusetts Institute of Technology2Harvard University

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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?

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Our Approach

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

Durable

Compliant

++

==

Simple+

Robust

Adaptive++

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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

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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

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Our Approach

• Incorporate behavior

– More DOFs than actuators• “Underactuated”

• Joints are coupled

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

Adaptive

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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?

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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 torque ratio τ2/τ1

• Examine effect on performance

Grasp Space

Object

Contact Forces

kbase

kmiddle

φ1

φ2

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Grasp Space

Object

Contact Forces

kbase

kmiddle

φ1

φ2

Simulation Result

Optimum torque ratio for poor sensing: τ2/τ1=~1

One actuator per hand performs as well as two!

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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

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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 Prototype

• 4 fingers

• 8 joints

• 1 actuator

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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

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Hand Properties

• Simple control– 4 fingers, 8 joints

– 1 motor!• Run to stall

– Feed-forward control

• Perform difficult tasks even with 3 positioning DOFs

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Hand Properties

• Simple control– 4 fingers, 8 joints

– 1 motor!• Run to stall

– Feed-forward control

• Perform difficult tasks even with 3 positioning DOFs

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Current Work

• SDM Hand as a prosthetic terminal device– Simple design makes it ideal for both body-

powered or myo-electrically controlled devices– Demonstrated adaptability is desirable– Molded construction can be mass-produced and

made to look realistic

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Acknowledgement

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

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Grasping in Human Environments

• Large sensing uncertainties– Object size, shape, location, etc. poorly known

• Grasping becomes difficult

• “Unplanned” contact– Large contact forces:

dislodge object, damage gripper– Grasp fails

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Our Overall Approach

• Focus on mechanical design of hands– Compensate for sensing uncertainties and

positioning errors– Durable hardware

• Minimal use of sensing/control

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Grasping in Unstructured Environments

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

Utah/MIT handrobonaut.jsc.nasa.gov

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Grasping in Unstructured Environments

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

Utah/MIT handrobonaut.jsc.nasa.gov

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Grasping in Unstructured Environments

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

They don’t work reliably

Utah/MIT handrobonaut.jsc.nasa.gov

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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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Grasping in Unstructured Environments

• Currently no attractive solution for humanoids and other robots to reliably grasp objects in the human environment!

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SDM Hand

• Simple– Feed-forward control

• Robust!– Immune to impacts– Good performance even

with bad sensing

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Hand Overview

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

environments

• Fabricated by hand using polymer-based Shape Deposition Manufacturing– Aluminum forearm

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Shape Deposition Manufacturing (SDM)

• Build part in layers• Alternate:

• Embed components– Protect fragile parts

• Heterogeneous materialsCourtesy Mark Cutkosky

Part and SupportMaterial Deposition

Material Removal (CNC machining)

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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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Fingers

• Single part– No fasteners or

adhesives!

• Lightweight (40g)

• Previous aluminum prototype: 60 parts (40 fasteners), 200g

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• Passively compliant– Large allowable deflections large positioning

errors• 3.5+ cm out-of-plane tip deflection w/o damage

– Low contact forces• Won’t disturb/damage object

• Viscoelastic joints– Damp out max joint deflection oscillations < 1 sec

Finger Properties

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• Hand shape, joint stiffnesses, and joint coupling were chosen based on optimization studies

Hand Configuration Optimization

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Hand Actuation Scheme

• Underactuated/Adaptive– # motors (DOAs) < # DOFs

• Tendon driven– In parallel with springs

• Joints compliant until

tendon tightens

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Hand Actuation Scheme

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

Harvard University

Hand Actuation Scheme

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

• Adaptable!

Harvard University

Hand Properties

• Simple control– 4 fingers, 8 joints, 1 motor!

• Run to stall

– Feed-forward control

• Perform difficult tasks even with 3 positioning DOFs

Harvard University

Hand Properties

• Simple control– 4 fingers, 8 joints

– 1 motor!• Run to stall

– Feed-forward control

• Perform difficult tasks even with 3 positioning DOFs

Harvard University

Hand Properties

• Robust– Immune to impacts

(Also dropped fingers

3x off 50ft. ledge –

no damage!)

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Hand Evaluation

• How do you evaluate grasping performance in an unstructured environment?

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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

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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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Future Work

• Add wrist, extend range of autonomous objects/tasks

• Investigate the role of sensing in grasping

• Dexterous Manipulation!

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Acknowledgments

• Thanks to the Cutkosky group at Stanford University for advice on SDM fabrication

• Supported by the Office of Naval Research grant number N00014-98-1-0669

Harvard University

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

iRobot’s PackBot

Durable Robotics

• Rarely addressed in robotics research– Essential for military, space, human environments

– Some locomotion, little manipulation

• In research, durability opens doors– Crashes don’t matter!

– Expands range of tasks that can be attempted

– Speeds implementation – reduces program validation

Utah/MIT hand

Univ. Minnesota’s Scout

Stanford/JPL hand

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Shape Deposition Manufacturing Process

magnets

connectors

Hallsensors

tendoncable

low-frictiontubes

Pockets with embedded componentsA CB

ED F

Dam material

Stiff polymer

New pockets

Soft polymersSoft polymers

Stiff polymer Complete fingers

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SDM robots

• Sprawl family of robots

• RiSE robots

[Introduction] Grasper Design Grasper Evaluation

Courtesy of Mark Cutkosky Courtesy of Mark Cutkosky

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Hand Actuation Scheme

• Underactuated/Adaptive– # motors < # DOFs

• Tendon driven– In parallel with springs

• Joints compliant until

tendon tightens

Optimum joint coupling:

~1:1 torque ratio

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Design Optimization

Object

RobotMotion

• Scenario (i.e. arbitrary assumptions)– Object ≈ circle (planar)– Sense approximate object location

(e.g. vision)– Move straight to object – Detect contact, stop robot– Close gripper

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

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Configuration Optimization

• Kinematics and stiffness design optimization – Simulate finger deflection as

object grasped – Varied joint rest angles

and joint stiffness ratio– Find largest successful Grasp

Space– Find maximum Contact Force

Grasp Space

Object

Contact Forces

RobotMotion

kbase

kmiddle

Harvard University

10 25 40 55 70 85

10

25

40

55

70

85

1

0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

10

25

40

55

70

85

2

10 25 40 55 70 85

1

10 25 40 55 70 85

1

10

25

40

55

70

85

k1/k2= 10

r/l=0.1

top contour = 0.45

top contour = 0.85

top contour = 0.95 top contour = 0.95 top contour = 0.95

top contour = 0.85 top contour = 0.85

top contour = 0.45 top contour = 0.45

(xc)max/l

max value = 0.99 max value = 0.99 max value = 0.99

max value = 0.86 max value = 0.86 max value = 0.86

max value = 0.46 max value = 0.46 max value = 0.46

A B

2

2

k1/k2= 1 k1/k2= 0.1

r/l=0.5

r/l=0.9

(xc)max/l

(xc)max/l. .

Configuration Optimization• Combine results:

Grasp range and Contact force• Optimum joint rest angles:

φ1,φ2=(25º,45º) • Optimum joint stiffness:

kbase<< kmiddle

Grasp Space

Stiff base jointStiff middle joint Equal joint stiffness

Middle Joint Rest Angle

10 25 40 55 70 85

10

25

40

55

70

85

1

0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

0

0.2

0.4

0.6

0.8

1

10

25

40

55

70

85

2

10 25 40 55 70 85

1

10 25 40 55 70 85

1

10

25

40

55

70

85

k1/k2= 10

r/l=0.1

top contour = 0.45

top contour = 0.85

top contour = 0.95 top contour = 0.95 top contour = 0.95

top contour = 0.85 top contour = 0.85

top contour = 0.45 top contour = 0.45

(xc)max/l

max value = 0.99 max value = 0.99 max value = 0.99

max value = 0.86 max value = 0.86 max value = 0.86

max value = 0.46 max value = 0.46 max value = 0.46

A B

2

2

k1/k2= 1 k1/k2= 0.1

r/l=0.5

r/l=0.9

(xc)max/l

(xc)max/l. .

Base Joint Rest Angle

Grasp Space

Object

Contact Forces

kbase

kmiddle

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Joint Coupling Optimization

Object

RobotMotion

• Object: – circle (planar), “unmovable”

• 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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Actuation Scheme

• To enable analysis, analyzed tendon-driven finger– Results of study apply to other

transmission methods

• One actuator per hand (4 joints)

Introduction [Grasper Design] Grasper Evaluation

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Grasp Scenario

[Introduction] Grasper Design Grasper Evaluation

Initial contact, no deflection

Begin actuationFinger 2 contact,force application

Object enclosure

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Actuation Optimization

• Vary joint torque ratio (distal:proximal)– Tendon routing + joint stiffnesses determine

joint torque ratio

• Find maximum Grasp Space, minimum Contact Forces

Introduction [Grasper Design] Grasper Evaluation

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Contact Force

Large ObjectSmall Object

Object location(distance

from hand center)

Torque Ratio middle/base

Grasp fails

Simulation Results

Tradeoff between low forces and large grasp range

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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

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

[Introduction] Grasper Design Grasper Evaluation

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Weighted Force

• Average over position and object radius

• Forces near expected position weighted more strongly

[Introduction] Grasper Design Grasper Evaluation

Better performance(lower forces)

torque ratio

forc

e qu

ality

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Weighted Grasp Space

• Weighted by probability of object within the grasp space

[Introduction] Grasper Design Grasper Evaluation

torque ratio

Better performance

Gra

sp s

pace

qua

lity

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Weighted Product

Noisy sensing

Good sensing

X

X

Optimum Torque Ratio:

• Product of the two quality measures

torque ratio

Betterperformance

Pro

duct

of

qual

ities

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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

[Introduction] Grasper Design Grasper Evaluation

www.barretttechnology.com

wwwrobot.gmc.ulaval.ca

Harvard University

Motivation

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

• Kaneko et al. 2005 – results particular to one specific grasper and task

• Birglen and Gosselin 2004 – Very good general framework for finger analysis, little consideration of object, grasping task

[Introduction] Grasper Design Grasper Evaluation

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 and

beginning Actuation

ii i

ik

for i=2,3,4

11

1

sin

coscx r

a

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Analysis

• Contact on link 3

3 1a a

3 3 3sin cos 0cont cont cr a x

xc

φ1

k2

k1

ψ3cont

a1a3

ψ4

ψ2

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Analysis

• Contact on outer links

12 4

1

2 tancont cont

r

l a

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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

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Overall Quality Measure

• Good sensing– Take maximum for

each torque ratio

Optimum at ~ 1:1

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Grasper Fabrication Process

magnets

connectors

Hallsensors

tendoncable

low-frictiontubes

Pockets with embedded componentsA CB

ED F

Dam material

Stiff polymer

New pockets

Soft polymersSoft polymers

Stiff polymer Complete fingers

Harvard University

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!