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This exploration highlights the evolution of embodied intelligence through iconic robotics, from Grey Walter's tortoises to MIT's social robots. It emphasizes the integral role of physical embodiment in cognitive development, illustrating how simple neural-inspired systems can exhibit complex behaviors. The discussion covers essential concepts such as situated intelligence, social interactions in robotic systems, and the foundational impact of the body on learning and knowledge. Key milestones, including the development of microbots and socially-guided learning systems, demonstrate the significance of embodiment in artificial intelligence.
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1948 — Grey Walter’s “Tortoises” • Simple neural net-inspired “brain” • Showed simple “brains” can lead to complex, life-like, intelligent behavior • Could find their way to recharging station • Simple (Pavlovian) learning • Similarities to “Braitenberg vehicles” (LCR, ch. 6) and Scribblers
Ancestors of the Scribbler Grey Walter’s tortoises Seymour Papert’s turtles turtle graphics the Scribbler
Importance ofEmbodied Intelligence • Traditional (dualist) view:mind is essentiallyindependent of the body • in principle, could have an intelligent “brain in a vat” • Now we understand that much of our knowledge is implicit in the fact that we have a body • Also, our body teaches us about the world • Structure of body is foundation for structure of knowledge • Knowledge is in the environment, rather than a representation of the environment
Structure of Embodied Intelligence • Representational primitives are skills, not concepts • Higher-level skills are built on lower-level • Lowest-level skills are grounded in the body
Embodied & Situated Artificial Intelligence • Therefore a genuine AI must be: • embedded in a body (embodied) • capable of interacting significantly with its world (situated) • Intelligence develops as consequence of interaction of body with environment, including other agents • How can we investigate embodied, situated intelligence?
1990s — “Ant” Microrobots (Rodney Brooks, MIT) • About 1 cubic inch • 17 sensors • Can communicate with each other • Goal: push limits of microrobotics • Goal: explore social interactions inspired by ant colony • Applications: explosives disposal, Mars exploration
1990s — Clustering Around “Food” • “Food” amongst other objects in environment • First “ant” to encounter food, signals others • Others cluster at food source
1990s — Tag Game • “It” robot wanders until bumps something • Transmits “Tag” • A “Not It” robot replies “I got tagged” • First becomes “Not It” • Second becomes “It”
1990s — Genghis (Brooks, MIT) • Subsumption architectureInspired by evolution: more complex behaviors build on simpler ones • Individual legs “do their jobs” • Legs are coordinated to achieve stability • Leg motion coordinated to achieve locomotion to goal
1990s — Genghis (Brooks, MIT) Front view & infrared sensing of person
1998 — Cog (Brooks, MIT) • “Humanoid intelligence requires humanoid interactions with the world” • Form of body is fundamental to cognitive representation • no “brains in vats” • Human-like intelligence requires human-like body
2001 — Leonardo • Cynthia Breazeal’s Lab, MIT • “Sociable Robots” Project • Vehicle for exploring socially guided learning & cooperative activity (video < Breazeal’s Lab)
2001 — Touch-Sensitive “Skin” • Touch-sensitive silicone skin over entire body • Mapped to neural net-like “homunculus” • Here Leo is programmed to notice & withdraw from contact (video < Breazeal’s Lab)
2004–7 — Socially Guided Learning • Leo is taught to “turn on all the lights” • Leo generalizes to new situation • Leo displays commitment to joint activity in spite of incorrect action (video < Breazeal’s Lab)
2004–7 — Collaborative tasks • Leo and human collaborate on a task (making sailboat and smiley face from colored blocks) • Have a common goal toward which the are working • Use gestures and social cues to cooperate
???? — Consciousness and self-awareness? Algorithms