Hebbian learning for online prediction, neural recall and classical conditioning of anthropomimetic robot arm motions

Abstract

Classical conditioning plays a vital role in learning in every mammal. It is based on unsupervised neural learning embodied in a physical body that is in continuous interaction with the environment. Embedding the hierarchical temporal memory (HTM) in the closed loop of the sensorimotor space of a Myorobotics tendon-driven robotic arm we demonstrate learning, prediction and control of biomimetic body motions. Experiments finally lead to conditioned reactions in natural interaction with a human partner. The HTM is able to learn arm movements generated by interaction with a human partner in a short time. It predicts future positions in different time scales up to seconds in advance. Closing the loop we utilize HTM predictions for motor control. Hereby learned motions are recalled from synaptic connections proactively continuing motion execution. Association, prediction and control are required by the HTM for conditioning according to Pavlov: neutral stimuli get associated with motions, and after learning sensor impulses can trigger single arm lifting motions. Hereby, both the motions and the stimuli are learned from the environment and get associated efficiently. We can demonstrate high biological plausibility as for example even input variations result in similar variations in the action output. The robotic system consisting of biologically-derived hardware and software components utilizes only unsupervised Hebbian learning to act autonomously. Learning is executed in real time, can handle natural variations of human motions and takes morphologically plausible sensor input into account. The setup is fully scalable due to its modularity. Hereby, novel applications for the HTM are opened: it can be used in musculoskeletal robot control scenarios and robots being able to interactively learn from human partners and the environment.