To date, although robot designers have achieved very precise movements, such movements are performed at very high speed, require strong forces and are power consuming. This approach cannot be applied to robots that interact with humans, as a malfunction might be potentially dangerous. To solve this challenge, University of Granada researchers have implemented a new cerebellar spiking model that adapts to corrections and stores their sensorial effects; in addition, it records motor commands to predict the action or movement to be performed by the robotic arm. This cerebellar model allows the user to articulate a state-of-the-art robotic arm with extraordinary mobility. The developers of the new cerebellar model have obtained a robot that performs automatic learning by extracting the input layer functionalities of the brain cortex.

A robotic set of legs that is able to fully model walking, in a biologically accurate manner, has been produced by a group of US researchers. The remarkably human-like walking gait was produced by simplifying the neural architecture, musculoskeletal architecture and sensory feedback pathways of humans, and building them into the robot. The biological accuracy of their robot has enabled the researchers to further investigate the processes that occur during walking, in humans. "And may bolster theories of how babies learn to walk, as well as helping to understand how spinal-cord-injury patients can recover the ability to walk." "A key component of the human walking system is the central pattern generator (CPG). The CPG is a neural network in the lumbar region of the spinal cord that generates rhythmic muscle signals. The CPG produces, and then controls, these signals by gathering information from different parts of the body that are responding to the environment. This is what allows people to walk without needing to think about it."