Clustering of qualitative contact states for a transmission assembly

Current manufacturing methods for robotic-controlled assembly rely on accurate positioning to ensure task completion, often through the use of special fixtures and precise calibration of the workspace. The reliance on precision positioning to achieve proper alignment creates problems in both programming and control of contact-based tasks. As a means of addressing these problems, we have been investigating the use of qualitative contact states (QCS) for modeling and learning low-level, force-based skills. Sensorimotor skills are modeled using force-based discrete states, which describe qualitatively how contact is being made with the environment. The qualitative states can be identified from force signals by viewing them as projected clusters in the force sensor space. In this paper, we investigate the automatic clustering of force data by applying a competitive agglomeration algorithm to extract clusters which can be used for QCS classifier training. Experimental results are included using an automotive transmission assembly