Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D. Howe 2 1 Massachusetts Institute of Technology 2 Harvard

Harvard University

Simple, Robust Grasping in

Unstructured Environments

Aaron Dollar1 and Robert D. Howe2

1Massachusetts Institute of Technology2Harvard University

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

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





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


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

They don’t work reliably


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• 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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• 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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• Adaptable!

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Hand Properties

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

• Run to stall



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Hand Properties





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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

Harvard University

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

Harvard University

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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• 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

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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


(xc)max/l

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



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



(xc)max/l




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

Harvard University

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


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


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Weighted Force

• Average over position and object radius

• Forces near expected position weighted more strongly


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


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

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

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


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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• Good sensing– Take maximum for

each torque ratio

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• 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!

Documents

Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D. Howe 2 1 Massachusetts Institute of Technology 2 Harvard