Antifragility as a complex system's response to perturbations, volatility, and time.

Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system's output response to input variability. Systems may respond poorly to perturbations (fragile) or benefit from perturbations (antifragile). In this manuscript, we review a range of applications of antifragility theory in technical systems (e.g., traffic control, robotics) and natural systems (e.g., cancer therapy, antibiotics). While there is a broad overlap in methods used to quantify and apply antifragility across disciplines, there is a need for precisely defining the scales at which antifragility operates. Thus, we provide a brief general introduction to the properties of antifragility in applied systems and review relevant literature for both natural and technical systems' antifragility. We frame this review within three scales common to technical systems: intrinsic (input-output nonlinearity), inherited (extrinsic environmental signals), and interventional (feedback control), with associated counterparts in biological systems: ecological (homogeneous systems), evolutionary (heterogeneous systems), and interventional (control). We use the common noun in designing systems that exhibit antifragile behavior across scales and guide the reader along the spectrum of fragility-adaptiveness-resilience-robustness-antifragility, the principles behind it, and its practical implications.

Robotic Light Touch Assists Human Balance Control During Maximum Forward Reaching.

OBJECTIVE: We investigated how light interpersonal touch (IPT) provided by a robotic system supports human individuals performing a challenging balance task compared to IPT provided by a human partner. BACKGROUND: IPT augments the control of body balance in contact receivers without a provision of mechanical body weight support. The nature of the processes governing the social haptic interaction, whether they are predominantly reactive or predictive, is uncertain. METHOD: Ten healthy adult individuals performed maximum forward reaching (MFR) without visual feedback while standing upright. We evaluated their control of reaching behavior and of body balance during IPT provided by either another human individual or by a robotic system in two alternative control modes (reactive vs. predictive). RESULTS: Reaching amplitude was not altered by any condition but all IPT conditions showed reduced body sway in the MFR end-state. Changes in reaching behavior under robotic IPT conditions, such as lower speed and straighter direction, were linked to reduced body sway. An Index of Performance expressed a potential trade-off between speed and accuracy with lower bitrate in the IPT conditions. CONCLUSION: The robotic IPT system was as supportive as human IPT. Robotic IPT seemed to afford more specific adjustments in the human contact receiver, such as trading reduced speed for increased accuracy, to meet the intrinsic demands and constraints of the robotic system or the demands of the social context when in contact with a human contact provider.

Stabilization of body balance with Light Touch following a mechanical perturbation: Adaption of sway and disruption of right posterior parietal cortex by cTBS.

Light touch with an earth-fixed reference point improves balance during quite standing. In our current study, we implemented a paradigm to assess the effects of disrupting the right posterior parietal cortex on dynamic stabilization of body sway with and without Light Touch after a graded, unpredictable mechanical perturbation. We hypothesized that the benefit of Light Touch would be amplified in the more dynamic context of an external perturbation, reducing body sway and muscle activations before, at and after a perturbation. Furthermore, we expected sway stabilization would be impaired following disruption of the right Posterior Parietal Cortex as a result of increased postural stiffness. Thirteen young adults stood blindfolded in Tandem-Romberg stance on a force plate and were required either to keep light fingertip contact to an earth-fixed reference point or to stand without fingertip contact. During every trial, a robotic arm pushed a participant's right shoulder in medio-lateral direction. The testing consisted of 4 blocks before TMS stimulation and 8 blocks after, which alternated between Light Touch and No Touch conditions. In summary, we found a strong effect of Light Touch, which resulted in improved stability following a perturbation. Light Touch decreased the immediate sway response, steady state sway following re-stabilization, as well as muscle activity of the Tibialis Anterior. Furthermore, we saw gradual decrease of muscle activity over time, which indicates an adaptive process following exposure to repetitive trials of perturbations. We were not able to confirm our hypothesis that disruption of the rPPC leads to increased postural stiffness. However, after disruption of the rPPC, muscle activity of the Tibialis Anterior is decreased more compared to sham. We conclude that rPPC disruption enhanced the intra-session adaptation to the disturbing effects of the perturbation.

Variable Impedance Control and Learning-A Review.

Robots that physically interact with their surroundings, in order to accomplish some tasks or assist humans in their activities, require to exploit contact forces in a safe and proficient manner. Impedance control is considered as a prominent approach in robotics to avoid large impact forces while operating in unstructured environments. In such environments, the conditions under which the interaction occurs may significantly vary during the task execution. This demands robots to be endowed with online adaptation capabilities to cope with sudden and unexpected changes in the environment. In this context, variable impedance control arises as a powerful tool to modulate the robot's behavior in response to variations in its surroundings. In this survey, we present the state-of-the-art of approaches devoted to variable impedance control from control and learning perspectives (separately and jointly). Moreover, we propose a new taxonomy for mechanical impedance based on variability, learning, and control. The objective of this survey is to put together the concepts and efforts that have been done so far in this field, and to describe advantages and disadvantages of each approach. The survey concludes with open issues in the field and an envisioned framework that may potentially solve them.

Gumpy: a Python toolbox suitable for hybrid brain-computer interfaces.

OBJECTIVE: The objective of this work is to present gumpy, a new free and open source Python toolbox designed for hybrid brain-computer interface (BCI). APPROACH: Gumpy provides state-of-the-art algorithms and includes a rich selection of signal processing methods that have been employed by the BCI community over the last 20 years. In addition, a wide range of classification methods that span from classical machine learning algorithms to deep neural network models are provided. Gumpy can be used for both EEG and EMG biosignal analysis, visualization, real-time streaming and decoding. RESULTS: The usage of the toolbox was demonstrated through two different offline example studies, namely movement prediction from EEG motor imagery, and the decoding of natural grasp movements with the applied finger forces from surface EMG (sEMG) signals. Additionally, gumpy was used for real-time control of a robot arm using steady-state visually evoked potentials (SSVEP) as well as for real-time prosthetic hand control using sEMG. Overall, obtained results with the gumpy toolbox are comparable or better than previously reported results on the same datasets. SIGNIFICANCE: Gumpy is a free and open source software, which allows end-users to perform online hybrid BCIs and provides different techniques for processing and decoding of EEG and EMG signals. More importantly, the achieved results reveal that gumpy's deep learning toolbox can match or outperform the state-of-the-art in terms of accuracy. This can therefore enable BCI researchers to develop more robust decoding algorithms using novel techniques and hence chart a route ahead for new BCI improvements.