Learning Objectives
Explain the Physical Symbol System Hypothesis vs. Connectionism debate and its stakes for AI
Map CL image schemas to robotic implementations including SayCan, RT-2, and PaLM-E
Understand Gibson's affordance concept and how it is implemented in embodied AI systems
Analyze the sim-to-real transfer challenge through the lens of CL embodiment theory
Where CL meets the physical world — how embodied AI systems ground abstract language in motor control and affordance.
This lesson builds on the theoretical foundations established earlier in the module and extends your understanding of how cognitive-linguistic principles apply directly to modern AI systems.
Learning Objectives
Explain the Physical Symbol System Hypothesis vs. Connectionism debate and its stakes for AI
Map CL image schemas to robotic implementations including SayCan, RT-2, and PaLM-E
Understand Gibson's affordance concept and how it is implemented in embodied AI systems
Analyze the sim-to-real transfer challenge through the lens of CL embodiment theory
Key Concepts
Explain the Physical Symbol System Hypothesis vs. Connectionism debate and its stakes for AI
Map CL image schemas to robotic implementations including SayCan, RT-2, and PaLM-E
Understand Gibson's affordance concept and how it is implemented in embodied AI systems
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