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.
Suction Leap-Hand: Suction Cups on a Multi-fingered Hand Enable Embodied Dexterity and In-Hand Teleoperation
School of Informatics, University of Edinburgh（信息学院，爱丁堡大学）；School of Physics, Engineering and Technology, University of York（物理、工程与技术学院，约克大学）；Tsinghua University（清华大学）
Decoding High-Dimensional Finger Motion from EMG Using Riemannian Features and RNNs
Neuromove, ULB Neuroscience Institute, ULB, Brussels, Belgium（Neuromove，ULB神经科学研究所，ULB，布鲁塞尔，比利时）；WEL Research Institute, Wavre, Belgium（WEL研究机构，瓦尔特，比利时）
