Segmentation via manipulation

A paradigm of iterative, interactive scene segmentation and simplification of random heaps of unknown objects via vision and manipulation is introduced. The scene simplification is based on the graph operations of vertex and edge removal. These operations are defined isomorphic to the pick and push manipulation actions. Sensors are used as graph generators and the manipulator is used as the decomposing mechanism of the graphs. The model is a nondeterministic finite-state Turing machine. A vision system, a manipulator, and force/torque and other sensory input are integrated into a robot work cell. Experiments conducted to test convergence and error recovery of four different strategies are discussed. It is found that under certain conditions the strategies can tolerate errors in the sensory data, recover from pathological states, and converge