On neuromechanical approaches for the study of biological and robotic grasp and manipulation
Valero-Cuevas and Santello Journal of NeuroEngineering and
Rehabilitation
On neuromechanical approaches for the study of biological and robotic grasp and manipulation
Francisco J. Valero-Cuevas 0 1
Marco Santello 2
0 Division of Biokinesiology & Physical Therapy, University of Southern California , Los Angeles, CA , USA
1 Biomedical Engineering Department, University of Southern California , Los Angeles, CA , USA
2 School of Biological and Health Systems Engineering Arizona State University , Tempe, AZ , USA
Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.
Neuromuscular control; Hand; Prosthetics
Introduction
Grasp and manipulation have captivated the imagination
and interest of thinkers of all stripes over the millennia;
and with enough reverence to even attribute the
intellectual evolution of humans to the capabilities of the hand
[
1–3
]. Simply put, manipulation function is one of the key
elements of our identity as a species (for an overview, see
[
4
]). This is a natural response to the fact that much of
our physical and cognitive ability and well-being is
intimately tied to the use of our hands. Importantly, we have
shaped our tools and environment to match its capabilities
(straightforward examples include lever handles, frets in
string instruments, and touch-screens). This co-evolution
between hand-and-world reinforces the notion that our
hands are truly amazing and robust manipulators, as well
as rich sensory, perceptual and even social information.
It then comes as no surprise that engineers and
physicians have long sought to replicate and restore this
functionality in machines—both as appendages to robots
and prostheses attached to humans with missing upper
limbs [
5
]. Robotic hands and prostheses have a long and
illustrious history, with records of sophisticated
articulated hands as early as Gottfried ‘Götz’ von Berlichingen’s
iron hand in 1504 [
6
]. Other efforts [
7–11
] were often
fueled by the injuries of war [
12–15
] and the Industrial
Revolution [16]. The higher survival rate in soldiers
who lose upper limbs [
17, 18
] and the continual
emergence of artificial intelligence [
19, 20
] are but the latest
impetus. Thus, the past 20 years have seen an explosion
in designs, fueled by large scale governmental funding
(e.g., DARPA’s Revolutionizing Prosthetics and HAPTIX
projects, EU’s INPUT and SOFTPRO projects) and
private efforts such as DeepMind. A new player in this space
is the potentially revolutionary social network of
highquality amateur scientists as exemplified by the FABLAB
movement [
21
]. They are enabled by ubiquitously accessible
and inexpensive 3D printing and additive
manufacturing tools [
22
], collaborative design databases (www.
eng.yale.edu/grablab/openhand/ and others), and
communities with formal journals (www.liebertpub.com/
overview/3d-printing-and-additive-manufacturing/621/
and www.journals.elsevier.com/additive-manufacturing/).
Grassroots communities have also emerged that can,
for example, compare and contrast the (...truncated)