A tendon-driven quadruped robot learned to track cyclic joint trajectories through the General-to-Particular algorithm, using motor babbling and refinement trials without a prior body model.
De novo motor learning creates structure in neural activity space that shapes adaptation,
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Hardware Implementation of a Zero-Prior-Knowledge Approach to Lifelong Learning in Kinematic Control of Tendon-Driven Quadrupeds
A tendon-driven quadruped robot learned to track cyclic joint trajectories through the General-to-Particular algorithm, using motor babbling and refinement trials without a prior body model.