Human–Robot Synchrony: Flexible Assistance Using Adaptive Oscillators

Abstract

We propose a novel method for movement assistance that is based on adaptive oscillators, i.e., mathematical tools that are capable of extracting the high-level features (amplitude, frequency, and offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexion-extension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps electromyography. Importantly, since the oscillator adapted to the movement imposed by the user, the method flexibly allowed us to change the movement pattern and was still efficient during the nonstationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require prespecifying a reference trajectory and does not require complex signal sensing or single-user calibration: the only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.

Rehabilitative Soft Exoskeleton for Rodents

“…One way of implementing such a compliant assistive controller is to measure the cyclic offset between the period of imposed trajectory and the actual period of the subject's reactive motion [45]. The closer the period motion of the coupled system is to the input period, the easier it is for the rodent to move, and the higher the force that is transmitted to the ground loading the foot.…”

Effects of Robotic Knee Exoskeleton on Human Energy Expenditure

“…This is also the main difference in comparison to the gravity compensation approach. Contrary to the position control approach, where the participants have to synchronize to the predefined periodic movement, here synchronization takes place between the neuromechanical oscillator, which is actually driving the joint and the artificial oscillator providing the assistance, similar as in [15]. The artificial oscillator also continuously adapted to the frequency and trajectory of the participants's squatting motion, e.g., the oscillator-based control approach always tries to adapt to the movement of the participant and not vice versa.…”

“…Banala et al [14], on the other hand, propose just gravity compensation. Ronsse et al [1], [15] propose a method based on adaptive frequency oscillators. The method is only applicable for periodic tasks.…”

Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

“…(2003), Gribovskaya and Billard (2008), Pastor et al (2009), Kober and Peters (2010)], rehabilitation (Ronsse et al (2010)), locomotion [e.g., Kimura et al (2007), Maufroy et al (2008)] and modular robotics [e.g., Cui et al (2010), Sproewitz et al (2010)] for instance. Here our focus is the generation of trajectories given simple, explicit high-level commands, as for instance in Maufroy et al (2008) for locomotion and Bullock and Grossberg (1988) and Hersch and Billard (2008) for reaching.…”