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Self-Organization, Embodiment, and Biologically Inspired Robotics Rolf Pfeifer, Max Lungarella , Fumiya Iida Science –

Self-Organization, Embodiment, and Biologically Inspired Robotics Rolf Pfeifer, Max Lungarella , Fumiya Iida Science – Nov 2007. Rakesh Gosangi PRISM lab Department of Computer Science and Engineering Texas A&M University. Outline. Embodiment Sensory-Motor Coordination

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Self-Organization, Embodiment, and Biologically Inspired Robotics Rolf Pfeifer, Max Lungarella , Fumiya Iida Science –

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  1. Self-Organization, Embodiment, and Biologically Inspired RoboticsRolf Pfeifer, Max Lungarella, Fumiya IidaScience – Nov 2007. Rakesh Gosangi PRISM lab Department of Computer Science and Engineering Texas A&M University

  2. Outline • Embodiment • Sensory-Motor Coordination • Embodiment and Information • Passive Dynamics • Designing Morphology • Future

  3. Embodiment • Embodied cognition • Human cognition is shaped by • Human morphology • Interaction with the environment • Example • Motor theory of speech perception • Embodied cognition contradicts computational theory of mind Embodied cognition Computational Mind

  4. Classical Robotics • Centralized control • Microprocessor • Information processing system • Disadvantages • Energetically inefficient • Cannot learn from interaction • Lack adaptivity

  5. Embodied Systems • Distribute the control • Controller • Morphology • Self-organization • Materials • Functional materials • Simplify neural control

  6. Outline • Embodiment • Sensory-Motor Coordination • Embodiment and Information • Passive Dynamics • Designing Morphology • Future

  7. Sensory-Motor coordination • Interactions where sensory stimulation influences motor actions and motor actions in turn influence the sensory stimulation. • Example – looking at an object in hand (foveation) • Dependence between sensor, neural, and motor variables Induces Sensory stimulation Movement Influences

  8. Properties of sensory-motor systems • Sensory and motor processes are coupled • Neither one is primary • Correlation between different sensory modalities • Temporal and spatial patterns in correlation • Characterize robot-environment interaction

  9. Examples • Salamander robot • Switching between swimming and walking (video) • Visual homing • How bees and wasps find their way back home • Phonotaxis • How female crickets identify male crickets in noisy environment

  10. Outline • Embodiment • Sensory-Motor Coordination • Embodiment and Information • Passive Dynamics • Designing Morphology • Future

  11. Information theoretic implications • Redundancy across sensory channels • Information structure develops with interaction with environment • Changes in morphology effect the information structure • Learning effects information structure • Learn cross-modal associations • Correlations between different sensory modalities

  12. Information self structuring • A – foveation • camera tracks the ball • B – random • Camera movement is unrelated to the ball Measures are applied to the camera image Images adapted from M. Lungarella, O. Sporns, PLoS Comp. Biol. 2, e144 (2006)

  13. Outline • Embodiment • Sensory-Motor Coordination • Embodiment and Information • Passive Dynamics • Designing Morphology • Future

  14. Passive Dynamics • “Intelligence by mechanics” • Intrinsic dynamics of the mechanical system yields self-stabilizing behavior • Select morphology and materials to exploit physical constraints in ecological niche • Examples

  15. Passive walking • Walking down a slope • Without control or actuation • Self stabilizing using gravity • Passive Walking • Walking on flat surfaces • Active power source to replace gravity • Reinforcement learning to find a policy that stabilizes the robot • Use less energy and control compared to powered robots • Passive Dynamic Walking

  16. Other examples • Ornithopters • Passive dynamics for wing rotation • Video • Waalbot • Adhesive materials like gecko • Video

  17. Outline • Embodiment • Sensory-Motor Coordination • Embodiment and Information • Passive Dynamics • Designing Morphology • Future

  18. Morphology • Evolutionary optimization of robot morphology • Current work on evolving the robot controller • “Morphofunctional” machines • Change functionality by modifying morphology • Increase adaptability, versatility and resilience

  19. Self reconfigurable robots • Macroscopic modules • Size of the modules constraint the morphology and functionality • Magnetic or mechanic docking interfaces • Self assembling modular robot • Simulation Video

  20. Outline • Embodiment • Sensory-Motor Coordination • Embodiment and Information • Passive Dynamics • Designing Morphology • Future

  21. Future • Imitation learning • Learn from humans and other robots reducing the search space • Collective robotics • With material and morphological considerations • Self-replicating robots • Machines that can autonomously construct a functional copy of themselves • John Von Neumann

  22. Thank you • Questions ? • Comments

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