robots that can work alongside humans
DESCRIPTION
Domo: Manipulation for Partner Robots Aaron Edsinger MIT Computer Science and Artificial Intelligence Laboratory Humanoid Robotics Group [email protected]. Robots That Can Work Alongside Humans. Built for human environments Safety in the human workspace - PowerPoint PPT PresentationTRANSCRIPT
Domo: Manipulation for Partner Robots
Aaron Edsinger
MIT Computer Science and Artificial Intelligence Laboratory
Humanoid Robotics [email protected]
Robots That Can Work Alongside Humans
• Built for human environments
• Safety in the human workspace
• Humanoid body to work with everday objects
• Perform tasks that are important to people using natural strategies with everyday objects
Confronting Unstructured Environments
Creating Robust Manipulation Interactions in Unstructured
Environments
• Let the body assist perception
• Passive compliance and force control
• Highly integrated behavior-based architecture
• Perceptual prediction through efference-copy models
• Learn task-relevant features of objects instead of using full 3D models
•29 active degrees of freedom (DOF)
•Two 6 DOF force controlled arms using Series Elastic Actuators
•Two 6 DOF force controlled hands using SEAs
•A 2 DOF force controlled neck using SEAs
•Stereo pair of Point Grey Firewire CCD cameras
•Stereo Videre STH-DCSG-VAR-C Firewire cameras
•Intersense 3 axis gyroscope
•Two 4 DOF hands using Force Sensing Compliant (FSC) actuators
•Embedded brushless and brushed DC motor drivers
•5 Embedded Motorola 56F807 DSPs running a 1khz control loop
•4 CANBus channels providing 100hz communication to external computation.
•49 potentiometers, 7 encoders, 24 tactile sensors, 12 brushless amplifiers, 17 brushed amplifiers, 12 sensor conditioners embedded on-board
•An estimated 500 fabricated mechanical components and 60 electronics PCBs
•12 node Debian Linux cluster running a mixture of C/C++/Python and utilizing the Yarp and pysense robot libraries.
•Weight: 42lbs. Height: 34" tall. Arm span: 5' 6"
Domo
VisualExploration
HandServoLeft
HandServoRight
HandLookRight
HandLookLeft
FaceTrack
BlobTrack
CartesianTrack
Fixation
VisualServo
Kinematics
ARB
PoseController TrackingController
ARB
BallTrack
ARB
I
ARB
Zero
I I I
FixationReach
CartesianTrack
ARB
ForceController VSpringController
ZeroG
ARB
VisualServoFingers
VisualServoProximity
I X
X
RelaxPose
ShowObject
StiffnessModulation
s
s
sx x
s
Behavior Based ArchitectureArm Behaviors Head Behaviors
Series Elastic and Force Sensing Compliant Actuators
F=-kx
Series Elastic and Force Sensing Compliant Actuators
•Mechanically simple
•Improved stability
•Shock tolerance
•Highly backdrivable
•Low-grade components
•Low impedance at high frequencies
Passive and Active Compliance
Series Elastic Actuator Force based grasping
Exploit interaction forces at the hand as an additional perceptual modality
Efference Copy Model
Y
Z
X
m 1r
m 0r
m 0l
m 1l
0r
1r
2r
3r
4r
5r
0l
1l
2l
3l
4l
5l
1h
0h
6r
7r
8r
9r
6l
7l
8l
9l
hT fJ
qqqqq
},,,{
},,,{
4321
4321
Upper 4 DOF of each arm.
Sensed joint torque
Sensed joint angle
Jacobian relates hand forces to joint torques
MotGravAccExoEgo
Mot
Acc
Exo
Ego
Sensed torque
Bimanual interaction torque
External interaction torque
Mass Acceleration torque
Motor torque
Z n
Z 2
Z 1
EFF
+- EC
Exo
Ego
ExoGrav
Mot
Commanded torqueSensed torque
Predicted torque
)(),()( qGqqVqqM Inverse dynamics
qqMAcc )(
0),( qqV Coriolis and Centrifugal
0
)(qGGrav
Efference Copy Model•Simplified inverse dynamic model of arm
•Model predicts normally occurring torques during reaching
•Use the prediction to amplify the salience of interaction torques (external and bimanual)
(von Holst, 1973)
Mot
Known (Commanded torque)
Known
Detection of Self-Induced Hand Forces
Interaction forces at hands are approximately equal and opposite
Interaction forces present
Interaction forces not present
Detection of Interaction Forces
Efference copy model generates torque prediction.
Torque prediction errors drivevisual attention system.
Ballistic reaching: prediction error
External forces: prediction error
Learning About Tool Use
•Motion feature points for tip detection•3D position estimation using probabilistic model
Estimation of Tool Position in the Hand
Autonomous Detection and
Control ofHuman Tools