EMG-Controlled Non-Anthropomorphic Hand Teleoperation Using a Continuous Teleoperation Subspace

TL;DR

This paper introduces a novel EMG-based teleoperation method for non-anthropomorphic robot hands, improving robustness and speed for pick-and-place tasks by projecting EMG signals into a specialized teleoperation subspace.

Contribution

It presents a new approach that maps EMG signals into a teleoperation subspace and combines predictors for robust control of non-anthropomorphic robot hands.

Findings

Faster task completion with the proposed method

More robust teleoperation compared to existing methods

Effective control of a multi-DOF non-anthropomorphic hand

Abstract

We present a method for EMG-driven teleoperation of non-anthropomorphic robot hands. EMG sensors are appealing as a wearable, inexpensive, and unobtrusive way to gather information about the teleoperator's hand pose. However, mapping from EMG signals to the pose space of a non-anthropomorphic hand presents multiple challenges. We present a method that first projects from forearm EMG into a subspace relevant to teleoperation. To increase robustness, we use a model which combines continuous and discrete predictors along different dimensions of this subspace. We then project from the teleoperation subspace into the pose space of the robot hand. Our method is effective and intuitive, as it enables novice users to teleoperate pick and place tasks faster and more robustly than state-of-the-art EMG teleoperation methods when applied to a non-anthropomorphic, multi-DOF robot hand.