Collective Learning Dynamics: Similarity Exploitation

This lecture explores a theory on collective learning dynamics, focusing on the transition from experimental observation to collective learning dynamics with similarity exploitation. Topics covered include structured learning versus deep end-to-end learning, adaptive impedance and force learning framework, system modeling, similarity modeling, and a collective learning model. The lecture also delves into a simulation study, experimental setup, and results showcasing the importance of sharing knowledge for exponential learning.