Creating Robust Manipulation Interactions with Imperfect Actuators and Sensors

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

Passive and Active Compliance Series Elastic Actuator Force based grasping

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

Force Based Efference-Copy Model

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

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 Ballistic reaching: prediction error Efference copy model generates torque prediction. Torque prediction errors drive visual attention system. External forces: prediction error

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

Examples Arm Behaviors Head Behaviors

Creating Robust Manipulation Interactions with Imperfect Actuators and Sensors