1. Domo: Manipulation for Partner Robots Aaron Edsinger MIT Computer Science and Artificial Intelligence Laboratory Humanoid Robotics Group

2. 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

3. Confronting Unstructured Environments

4. 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

5. Domo 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

6. Behavior Based Architecture
Arm Behaviors
Head Behaviors

7. Series Elastic and Force Sensing Compliant Actuators
F=-kx

8. Series Elastic and Force Sensing Compliant Actuators
Mechanically simple
Improved stability
Shock tolerance
Highly backdrivable
Low-grade components
Low impedance at high frequencies

9. Passive and Active Compliance
Series Elastic Actuator Force based grasping

10. Efference Copy Model
Exploit interaction forces at the hand as an additional perceptual modality
Upper 4 DOF of each arm.
Sensed joint torque
Sensed joint angle
Jacobian relates hand forces
to joint torques

11. Efference Copy Model Simplified inverse dynamic model of arm
Sensed torque
Efference Copy Model
Bimanual interaction torque
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)
External interaction torque
Mass Acceleration torque
Motor torque
Inverse dynamics
Coriolis and Centrifugal
Predicted torque
Known (Commanded torque)
Sensed torque
Commanded torque
Known
(von Holst, 1973)

12. Detection of Self-Induced Hand Forces
Interaction forces at hands are approximately equal and opposite
Interaction forces present
Interaction forces not present

13. Detection of Interaction Forces
Ballistic reaching: prediction error
Efference copy model generates
torque prediction.
Torque prediction errors drive
visual attention system.
External forces: prediction error

14. Learning About Tool Use
Motion feature points for tip detection
3D position estimation using probabilistic model

15. Estimation of Tool Position in the Hand

16. Autonomous Detection and Control of Human Tools