Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D. Howe 2 1 Massachusetts Institute of Technology 2 Harvard 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 k base k middle φ1φ1 φ2φ2

Harvard University Grasp Space Object Contact Forces k base k middle φ1φ1 φ2φ2 Simulation Result Optimum joint rest angles: φ 1,φ 2 =(25º,45º) Optimum joint stiffness: k base << k middle –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 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 τ 2 /τ 1 Examine effect on performance Grasp Space Object Contact Forces k base k middle φ1φ1 φ2φ2

Harvard University Grasp Space Object Contact Forces k base k middle φ1φ1 φ2φ2 Simulation Result Optimum torque ratio for poor sensing: τ 2 /τ 1 =~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!

Harvard University Tendon Actuation Scheme Tendons in parallel with compliance  much stiffer when actuated –Soft during exploration, acquisition –Stiff, stable grasp

Harvard University 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 N

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 hand robonaut.jsc.nasa.gov

Harvard University Grasping in Unstructured Environments Complex hands = Complicated! –Difficult to control –Expensive –Fragile Utah/MIT hand robonaut.jsc.nasa.gov

Harvard University Grasping in Unstructured Environments Complex hands = Complicated! –Difficult to control –Expensive –Fragile They don’t work reliably Utah/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 unsuccessful Utah/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

Harvard University Shape Deposition Manufacturing (SDM) Build part in layers Alternate: Embed components –Protect fragile parts Heterogeneous materials Courtesy Mark Cutkosky Part and Support Material 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

Harvard University 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)

Harvard University Experiment 1 Grasp range results –PVC tube ±5cm in x 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 center ±2cm in y ~45% of object size Wood block x y

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 N

Harvard University

Call for Papers Robot Manipulation: Sensing and Adapting to the Real World Workshop at Robotics: Science and Systems 2007 Atlanta, 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 Hall sensors tendon cable low-friction tubes Pockets with embedded components A CB E D F Dam material Stiff polymer New pockets Soft polymers Stiff polymerComplete fingers

Harvard University SDM robots Sprawl family of robots RiSE robots [Introduction] Grasper Design Grasper Evaluation 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 Robot Motion 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 Robot Motion k base k middle

Harvard University Configuration Optimization Combine results: Grasp range and Contact force Optimum joint rest angles: φ 1,φ 2 =(25º,45º) Optimum joint stiffness: k base << k middle Grasp Space Stiff base jointStiff middle jointEqual joint stiffness Middle Joint Rest Angle Base Joint Rest Angle Grasp Space Object Contact Forces k base k middle

Harvard University Joint Coupling Optimization Object Robot Motion Object: –circle (planar), “unmovable” General scenario: 1. Sense approximate object location (e.g. vision) 2. Move straight to object 3. Detect contact, stop robot 4. 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 actuation Finger 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 force quality

Harvard University Weighted Grasp Space Weighted by probability of object within the grasp space [Introduction] Grasper Design Grasper Evaluation torque ratio Better performance Grasp space quality

Harvard University Weighted Product Noisy sensing Good sensing X X Optimum Torque Ratio: Product of the two quality measures torque ratio Better performance Product of qualities

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 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 – 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 2007 Atlanta, GA, USA submission deadline - May 1st notification of acceptance - May 15th workshop - June 30th

Harvard University Analysis Initial contact and beginning Actuation for i=2,3,4

Harvard University Analysis Contact on link 3 xcxc φ1φ1 k2k2 k1k1 ψ 3cont a1a1 a3a3 ψ4ψ4 ψ2ψ2

Harvard University Analysis Contact on outer links

Harvard University Overall Quality Measure Good sensing –Average doesn’t make sense –No predetermined x t 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 Hall sensors tendon cable low-friction tubes Pockets with embedded components A CB E D F Dam material Stiff polymer New pockets Soft polymers Stiff polymerComplete 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!