Monitoring contact using clustering and discriminant functions

Many robotic tasks are easily described using discrete event dynamic systems. However, the robot sensory and control systems operate in the continuous domain, leading to the problem of associating states of the continuous system with the states and events (changes in state) in the discrete task space. This paper presents a new approach to discretizing sensory data, based on discriminant functions and clustering techniques, for applications in robotic process monitoring and in interpreting human sensory data. The discriminant functions are learned from real sensory data, and hence the approach has the advantages of being adaptive, and also of taking into account various task parameters such as friction. Most importantly, the approach can be adapted quickly to different tasks by simply learning a new set of discriminant functions from sensory data corresponding to the task. Experimental results are presented to demonstrate the effectiveness of this approach