@inproceedings {pub4368,
	title = {Learning Hybrid Models for Variable Impedance Control of Changing-Contact Manipulation Tasks},
	author = {Saif Sidhik AND Mohan Sridharan AND Dirk Ruiken},
	year = {2020},
	month = {August},
	abstract = {Many robot manipulation tasks comprise discrete action sequences characterized by continuous dynamics, while the transitions between these discrete dynamic modes are characterized by discontinuous dynamics.  The individual modes can represent different types of contacts, surfaces, or other factors, and different control strategies may be needed for each mode and the transitions between the modes. This paper describes a piece-wise continuous, hybrid control framework for such manipulation tasks. The underlying representation enables the robot to automatically and efficiently detect the transitions between known modes, recognize new modes, and incrementally learn a dynamics model for variable impedance (i.e., stiffness) control in each mode, invariant to the direction of motion and the magnitude of applied forces. The framework is evaluated on a robot manipulator sliding an object along a surface to achieve a desired motion trajectory in the presence of changes in surface friction, applied force, or the type of contact between the object and the surface.},
	publisher = {Cognitive Systems Foundation},
	booktitle = {Conference on Advances in Cognitive Systems 2020}
}