Self-Organization, Embodiment, and Biologically Inspired Robotics

Rolf Pfeifer, Max Lungarella, Fumiya IidaScience – Nov 2007.

Rakesh GosangiPRISM lab

Department of Computer Science and Engineering

Texas A&M University

Outline

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

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

Computational Mind

Embodied cognition

Classical Robotics

• Centralized control– Microprocessor– Information processing system

• Disadvantages– Energetically inefficient– Cannot learn from interaction– Lack adaptivity

Embodied Systems

• Distribute the control– Controller– Morphology

• Self-organization

– Materials• Functional materials• Simplify neural control

Outline

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

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

Movement

Sensory stimulation

Induces

Influences

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

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

Outline

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

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

Information self structuring

• A – foveation– camera tracks the ball

• B – random– Camera movement is unrelated

to the ballMeasures are applied to the camera image

Images adapted from M. Lungarella, O. Sporns, PLoS Comp. Biol. 2, e144 (2006)

Outline

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

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

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

Other examples

• Ornithopters– Passive dynamics for wing rotation– Video

• Waalbot– Adhesive materials like gecko– Video

Outline

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

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

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

Outline

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

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

Thank you

• Questions ?• Comments