AHaH Computing–From Metastable Switches to Attractors to Machine Learning
The robotic arm challenge involves a multi-jointed robotic arm that moves to capture a target. Each joint on the arm has 360 degrees of rotation, and the base joint is anchored to the floor. Using only a value signal relating the distance from the head to the target and an AHaH motor controller taking as input sensory stimuli in a closed-loop configuration, the robotic arm autonomously learns to capture stationary and moving targets. New targets are dropped within the arm’s reach radius after each capture, and the number of discrete angular joint actuations required for each catch is recorded to asses capture efficiency.