Determining proper grasp configurations for handovers through observation of object movement patterns and inter-object interactions during usage

We present a method for enabling robots to determine appropriate grasp configurations for handovers - i.e., where to grasp, and how to orient an object when handing it over. In our method, a robot first builds a knowledge base by observing demonstrations of how certain objects are used and their proper handover grasp configurations. Objects in the knowledge base are then organized based on their movements and inter-object interaction features. The key point in this process is that similarity in affordances should be recognized. When subsequently asked to handover an object, the robot then computes an appropriate grasp configuration based on the object's recognized affordances. Experimental results show that our method was able to differentiate and group together objects according to their affordances. Furthermore, when given a new object, our method was able to generalize data in the knowledge base and determine an appropriate grasp configuration.