A Continuous Teleoperation Subspace With Empirical and Algorithmic Mapping Algorithms for Nonanthropomorphic Hands

Teleoperation is a valuable tool for robotic manipulators in highly unstructured environments. However, finding an intuitive mapping between a human hand and a nonanthropomorphic robot hand can be difficult, due to the hands’ dissimilar kinematics. In this article, we seek to create a mapping between the human hand and a fully actuated, nonanthropomorphic robot hand that is intuitive enough to enable effective real-time teleoperation, even for novice users. To accomplish this, we propose a low-dimensional teleoperation subspace that can be used as an intermediary for mapping between hand pose spaces. We present two different methods to define the teleoperation subspace: an empirical definition, which requires a person to define hand motions in an intuitive, hand-specific way, and an algorithmic definition, which is kinematically independent and uses objects to define the subspace. We use each of these definitions to create a teleoperation mapping for different hands. One of the main contributions of this article is the validation of both the empirical and algorithmic mappings with teleoperation experiments controlled by ten novices and performed on two kinematically distinct hands. The experiments show that the proposed subspace is relevant to teleoperation, intuitive enough to enable control by novices, and can generalize to nonanthropomorphic hands with different kinematics.
Note to Practitioners
—As robots move into our warehouses, workplaces, and homes, it is important to develop robotic controls that are intuitive and easy for novices to use. In particular, teleoperation can be valuable to guide robots when they encounter situations that autonomous programs are not prepared to deal with. In this article, we focus specifically on robotic grasping using nonanthropomorphic hands. Our method is intended for novice users to intuitively teleoperate such robots. We show that the teleoperation subspace we use can effectively enable pick-and-place tasks and in-hand manipulation tasks and that it is intuitive for novice operators. Our subspace outperforms state-of-the-art methods for pick-and-place tasks and performs as well as state-of-the-art methods for in-hand manipulation.