Learning to predict how rigid objects behave under simple manipulation

We are investigating the problem of predicting how objects behave under manipulative actions. In particular, we wish to predict the workpiece motions which will result from simple pushing manipulations by a single robotic fingertip. Such interactions are themselves fundamental components of multi-fingered grasping and other complex interactions. Physics simulators can be used to do this, but they model many kinds of object interactions poorly, being dependent on detailed scene descriptions and parameters, which in practice are often difficult to tune. Additionally, we have previously investigated ways of learning to predict, by employing density estimation techniques to learn, from many example pushes, a probabilistic mapping between applied pushing motions and resulting workpiece motions. In contrast, this paper presents an alternative approach to prediction, which does not rely on learning but infers the likelihood of possible workpiece motions by using the simple physics principle of minimum energy. This approach is advantageous in situations where insufficient prior knowledge is available for training our learned predictors. In such situations, possible strategies include either training learned predictors on unrealistic simulation data, or making use of the simple physics approach which requires no training. We show that the second of these strategies performs significantly better, and approaches the performance of learned predictors are trained on observations of real object motions.