This work presents the concept of tele-impedance as a method for remotely controlling a robotic arm in interaction with uncertain environments. As an alternative to bilateral force-reflecting teleoperation control, in tele-impedance a compound reference command is sent to the slave robot including both the desired motion trajectory and impedance profile, which are then realized by the remote controller without explicit feedback to the operator. We derive the reference command from a novel body–machine interface (BMI) applied to the master operator’s arm, using only non-intrusive position and electromyography (EMG) measurements, and excluding any feedback from the remote site except for looking at the task. The proposed BMI exploits a novel algorithm to decouple the estimates of force and stiffness of the human arm while performing the task. The endpoint (wrist) position of the human arm is monitored by an optical tracking system and used for the closed-loop position control of the robot’s end-effector. The concept is demonstrated in two experiments, namely a peg-in-the-hole and a ball-catching task, which illustrate complementary aspects of the method. The performance of tele-impedance control is assessed by comparing the results obtained with the slave arm under either constantly low or high stiffness.

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive PNS-Machine Interfaces was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PNS-Machine Interface (PMI) has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the peripheral nervous system (PNS) in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

In this paper, two upper limbs of an exoskeleton robot are operated within a constrained region of the operational space with unidentified intention of the human operator’s motion as well as uncertain dynamics including physical limits. The new human-cooperative strategies are developed to detect the human subject’s movement efforts in order to make the robot behavior flexible and adaptive. The motion intention extracted from the measurement of the subject’s muscular effort in terms of the applied forces/torques can be represented to derive the reference trajectory of his/her limb using a viable impedance model. Then, adaptive online estimation for impedance parameters is employed to deal with the nonlinear and variable stiffness property of the limb model. In order for the robot to follow a specific impedance target, we integrate the motion intention estimation into a barrier Lyapunov function based adaptive impedance control. Experiments have been carried out to verify the effectiveness of the proposed dual-arm coordination control scheme, in terms of desired motion and force tracking.