Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D....
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Transcript of Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D....
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++
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
Harvard University
Optimization Goal
• Find the hand configuration that leads to largest Successful Grasp Space with minimum Contact Forces Grasp Space
Object
Contact Forces
Harvard University
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
Harvard University
Motivation
• How should joints be coupled for good grasping performance?
Harvard University
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
Harvard University
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!
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
Tendon cable
Soft fingerpads
Viscoelastic flexure joints
Stiff links
Hollow cable raceway
Dovetail connector
2cm
Embedded cable anchor
Harvard University
Mechanism Behavior
Harvard University
Grasper Prototype
• 4 fingers
• 8 joints
• 1 actuator
Harvard University
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
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
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
Harvard University
Acknowledgement
This work was supported by the Office of Naval Research grant number N00014-98-1-0669.
Harvard University
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
Harvard University
Our Overall Approach
• Focus on mechanical design of hands– Compensate for sensing uncertainties and
positioning errors– Durable hardware
• Minimal use of sensing/control
Harvard University
Grasping in Unstructured Environments
• Traditional approach: Complex hands– Many DOFs and DOAs– 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 reliably
Utah/MIT handrobonaut.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
Harvard University
Grasping in Unstructured Environments
• Currently no attractive solution for humanoids and other robots to reliably grasp objects in the human environment!
Harvard University
SDM Hand
• Simple– Feed-forward control
• Robust!– Immune to impacts– Good performance even
with bad sensing
Harvard University
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)
Harvard University
Tendon cable
Soft fingerpads
Viscoelastic flexure joints
Stiff links
Hollow cable raceway
Dovetail connector
2cm
Embedded cable anchor
Harvard University
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
Harvard University
• Hand shape, joint stiffnesses, and joint coupling were chosen based on optimization studies
Hand Configuration Optimization
Harvard University
Hand Actuation Scheme
• Underactuated/Adaptive– # motors (DOAs) < # DOFs
• Tendon driven– In parallel with springs
• Joints compliant until
tendon tightens
Harvard University
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!)
Harvard University
Hand Evaluation
• How do you evaluate grasping performance in an unstructured environment?
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
Harvard University
Experimental Platform
• Hand mounted on WAM robot arm– 3 DOF– No wrist!
• No orientation control
Harvard University
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
Harvard University
Experiment 2
• Autonomous grasping across workspace
• Guided by single image– Simple USB webcam
• 640x480 resolution
– Looking down on workspace
Harvard University
Future Work
• Add wrist, extend range of autonomous objects/tasks
• Investigate the role of sensing in grasping
• Dexterous Manipulation!
Harvard University
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
Harvard University
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
Harvard University
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
Harvard University
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
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
Harvard University
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
Harvard University
Grasp Scenario
[Introduction] Grasper Design Grasper Evaluation
Initial contact, no deflection
Begin actuationFinger 2 contact,force application
Object enclosure
Harvard University
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
Harvard University
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
Harvard University
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
Harvard University
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
Harvard University
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
Harvard University
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
[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
Harvard University
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
Harvard University
Analysis
• Contact on outer links
12 4
1
2 tancont cont
r
l a
Harvard University
Overall Quality Measure
• Good sensing– Average doesn’t make
sense
– No predetermined xt
• Can target according to object size
Harvard University
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 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!