Forecasting Bimanual Object Manipulation Sequences from Unimanual Observations

Abstract

Learning to forecast bimanual object manipulation sequences from unimanual observations has broad applications in assistive robots and augmented reality. This challenging task requires us to first infer motion from the missing arm and the object it would have been manipulating were the person bimanual, then forecast the human and object motion while maintaining hand-object contact during manipulation. Previous attempts model the hand-object interactions only implicitly, and thus tend to produce unrealistic motion where the objects float in air. We address this with a novel neural network that (i) identifies and forecasts the pose for only the objects undergoing motion through an object motion module and (ii) refines human pose predictions by encouraging hand-object contact during manipulation through an ensemble of human pose predictors. The components are also designed to be generic enough for use in both unimanual and bimanual contexts. Our approach outperforms the state-of-the-art pose forecasting methods on bimanual manipulation datasets.