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Creating Robust Manipulation Interactions with Imperfect Actuators and Sensors

This study focuses on improving manipulation interactions utilizing force interactions for learning opportunities. It explores passive and active compliance with Series Elastic Actuators, exploiting force interactions for enhanced learning and decreasing shoulder stiffness. The force-based representation of object affordances and the Force-Based Efference-Copy Model predict and amplify salient interaction forces during manipulation, aiding in richer manipulation abilities. The architecture includes behavioral primitives for tightly integrated systems and a 100Hz scheduler for dynamic arbitration.

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Creating Robust Manipulation Interactions with Imperfect Actuators and Sensors

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  1. Creating Robust Manipulation Interactions with Imperfect Actuators and Sensors • Passive and active compliance with SEAs • Highly integrated set of behaviors through behavior-based architecture • Perceptual saliency amplification through efference-copy models • Exploit rich force interactions which naturally occur during exploration as a learning opportunity

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

  3. Exploiting Force Interactions for Learning • Exploration behavior • Decrease shoulder stiffness on contact • Localizes exploration around object • Creates rich interaction forces • Learning • Force based representation of object affordances • Model of natural interaction forces • Scaffold to richer manipulation abilities

  4. Force Based Efference-Copy Model

  5. Force Based Efference-Copy Model • Predictive forward model of the joint torques • Amplifies salient interaction forces during manipulation • Torque predictions made using simple kinematic and mass model Predicted torque Sensed torque Commanded torque

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

  7. 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

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

  9. Examples Arm Behaviors Head Behaviors

  10. Examples Arm Behaviors Head Behaviors

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