1. 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

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

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

4. Classical Robotics Centralized control Disadvantages 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 Visual homing Phonotaxis
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 Walking on flat surfaces
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 Waalbot Passive dynamics for wing rotation
Video
Waalbot
Adhesive materials like gecko

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 Collective robotics Self-replicating robots
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