Neuromuscular Interfacing for Advancing Kinesthetic and Teleoperated Programming by Demonstration of Collaborative Robots.

This study addresses the challenges of Programming by Demonstration (PbD) in the context of collaborative robots, focusing on the need to provide additional degrees of programming without hindering the user's ability to demonstrate trajectories. PbD enables an intuitive programming of robots through demonstrations, allowing non-expert users to teach robot skills without coding. The two main PbD modalities, observational and kinesthetic, have limitations when it comes to programming the diverse functionalities offered by modern collaborative robots.

Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information.

Two sub-problems are typically identified for the replication of human finger motions on artificial hands: the measurement of the motions on the human side and the mapping method of human hand movements (primary hand) on the robotic hand (target hand). In this study, we focus on the second sub-problem. During human to robot hand mapping, ensuring natural motions and predictability for the operator is a difficult task, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values.

A Control Architecture for Grasp Strength Regulation in Myocontrolled Robotic Hands Using Vibrotactile Feedback: Preliminary Results.

Nowadays, electric-powered hand prostheses do not provide adequate sensory instrumentation and artificial feedback to allow users voluntarily and finely modulate the grasp strength applied to the objects. In this work, the design of a control architecture for a myocontrol-based regulation of the grasp strength for a robotic hand equipped with contact force sensors is presented. The goal of the study was to provide the user with the capability of modulating the grasping force according to target required levels by exploiting a vibrotactile feedback.

Experimental evaluation of a sEMG-based control for elbow wearable assistive devices during load lifting tasks.

In this work, a surface skin electromyography(sEMG)-based control solution for elbow wearable assistive devices during load lifting tasks is presented. The goal of the controller consists in limiting the user's muscle activity during the task execution, in such a way that the assistive device can partially compensate the load-related biceps muscle effort. Since sEMG-driven control strategies based on the estimation of the joint torques generally requires complex task- and subject-dependent training sessions for tuning the control algorithms, here a more direct control approach is proposed, based on a muscle activity error related proportional-integral action together with an double-threshold activation logic.