@inproceedings {pub4027,
	title = {Online Learning of Feed-Forward Models for Task-Space Variable Impedance Control},
	author = {Michael  J. Mathew AND Saif Sidhik AND Mohan Sridharan AND Morteza Azad AND Akinobu Hayashi AND Jeremy Wyatt},
	year = {2019},
	month = {October},
	abstract = {During the initial trials of a novel manipulation task, humans tend to keep their arms considerably stiff in order to reduce the effects of any unforeseen disturbances on the ability to perform the task accurately. After a few repetitions, humans reduce and adapt the stiffness of their arms without any significant reduction in task performance. Research in human motor control strongly indicates that humans learn and continuously revise internal models of manipulation tasks to support such adaptive behaviour. These internal models help predict future states of the task, anticipate necessary control actions, and adapt impedance quickly to match task requirements. Drawing inspiration from these findings, we propose a novel framework that supports the online learning of a time-independent forward model of a manipulation task from a small number of examples. The measured inaccuracies in the predictions of this model are used to dynamically update the forward model and modify the impedance parameters of a feedback controller during task execution. Furthermore, our framework includes a hybrid force-motion controller that enables the robot to be compliant in particular directions (if required) while adapting the impedance in other directions. These capabilities are illustrated and evaluated on continuous contact tasks such as polishing a board and stirring porridge.},
	publisher = {IEEE},
	booktitle = {IEEE-RAS International Conference on Humanoid Robots}
}