1 / 19

1.26.2006

Domo: Partner Robots for Manipulation Aaron Edsinger MIT Computer Science and Artificial Intelligence Laboratory Humanoid Robotics Group edsinger@csail.mit.edu. 1.26.2006. Robots That Can Work Alongside Humans. Built for human environments Safety in the human workspace

caden
Download Presentation

1.26.2006

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Domo: Partner Robotsfor ManipulationAaron EdsingerMIT Computer Science and Artificial Intelligence LaboratoryHumanoid Robotics Groupedsinger@csail.mit.edu 1.26.2006

  2. Robots That Can Work Alongside Humans • Built for human environments • Safety in the human workspace • Humanoid body to work with everyday objects • Perform tasks that are important to people using natural strategies with everyday objects

  3. Robots That Can Work Alongside Humans • Human-robot interface: use the body, not the keyboard. • Apply human expertise: coach the robot, through physical, visual, and auditory guidance.

  4. Robots That Can Work Alongside Humans

  5. Confronting Unstructured Environments

  6. Robust Manipulation and Human Interactions in Unstructured Environments • Active Perception: Use the robot body to assist perception • Human Safety: Passive compliance and force control • Integration: Architecture to support rich, tightly coupled behaviors • Perceptual constraints: Focus resources on only task-relevant features of objects and interactions

  7. 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 Domo

  8. 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 • 15 node Debian Linux cluster running a mixture of C/C++/Python and utilizing the Yarp and pysense robot libraries. Domo

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

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

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

  12. Domo: Behavior Based Architecture • Architectural primitives allow tightly integrated system • 100hz scheduler • Dynamic arbitration • ~15 node Linux cluster • ~50 threads currently Homeostat

  13. Architecture Example Arm Behaviors Head Behaviors

  14. Behavior Based Architecture Arm Behaviors Head Behaviors

  15. Detection of Human Interaction Forces Interaction forces at hands are approximately equal and opposite Interaction forces present Interaction forces not present

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

  17. Learning Tool Use By Demonstration • Motion feature points for tip detection • 3D position estimation using probabilistic model

  18. Estimation of Tool Position in the Hand

  19. Autonomous Detection and Control ofHuman Tools by Demonstration

More Related