A Comparison of Autoregressive Hidden Markov Models for Multi-Modal Manipulations with Variable Masses

Abstract

In contact-based manipulations, the effects of the robot’s actions change as contacts are made or broken. For example, if a robot applies an increasing upward force to an object, then the force will eventually overcome the object’s weight and break the object-table contact. The robot can subsequently raise or lower the height of the object. The transition from resting on the table to not being in contact with the table is an example of a mode switch. The conditions for this mode switch depend on the mass of the object being manipulated. By modeling the mode switch, the robot can estimate the mass of the object based on the conditions when the mode switch occurs. The robot can also use the model to predict when the object will break contact given its mass.

We evaluated four different autoregressive hidden Markov models for representing manipulations with mass-dependent mode switches. The models were successfully evaluated on pushing and lifting tasks. The evaluations show that the predicted object trajectories and estimated object masses are more accurate when using models that interpolate between different masses, and that consider the observed state for estimating the mode switches.