On the role of stability in animal morphology and neural control

Mechanical stability is vital for the fitness and survival of animals and is a crucial aspect of robot design and control. Stability depends on multiple factors, including the body\u27s intrinsic mechanical response and feedback control. But feedback control is more fragile than the body\u27s innate mechanical response or open-loop control strategies because of sensory noise and time-delays in feedback. This thesis examines the overarching hypothesis that stability demands have played a crucial role in how animal form and function arise through natural selection and motor learning. In two examples, finger contact and overall body stability, we investigated the relationship between morphology, open-loop control, and stability. By studying the stability of the internal degrees of freedom of a finger when pushing on a hard surface, we find that stability limits the force that we can produce and is a dominant aspect of the neural control of the finger\u27s muscles. In our study on whole body lateral stability during locomotion in terrestrial animals, we find that the overall body aspect ratio has evolved to ensure passive lateral stability on the uneven terrain of natural environments. Precisely gripping an object with the fingertips is a hallmark of human hand dexterity. In Chapter 2, we show how human fingers are intrinsically prone to a buckling-type postural instability and how humans use careful neural orchestration of our muscles so that the elastic response of our muscles can suppress the intrinsic instability. In Chapter 3, we extend these findings further to examine the nature of neuromuscular variability and how the nervous system deals with the need for muscle-induced stability. We find that there is structure to neuromuscular variability so that most of the variability lies within the subspace that does not affect stability. Inspired by the open-loop stable control of our index fingers, in Chapter 4, we derive open-loop stability conditions for a general mechanical linkage with arbitrary joint torques subjected to holonomic constraints. The solution that we derive is physically realizable as cable-driven active mechanical linkages. With a user-prescribed cable layout, we pose the problem of actuating the system to maintain stability while subject to goals like energy minimization as a convex optimization problem. We are thus able to use efficient optimization methods available for convex problems and demonstrate numerical solutions in examples inspired by the finger. Chapter 5 presents a general formulation of the stability criteria for active mechanical linkages subject to Pfaffian holonomic and non-holonomic constraints. Active mechanical linkages subject to multiple constraints represent the mechanics of systems spanning many domains and length scales, such as limbs and digits in animals and robots, and elastic networks like actin meshes in microscopic systems. We show that a constrained mechanical linkage with regular stiffness and damping, and circulation-free feedback, can only destabilize by static buckling when subject to holonomic constraints. In contrast, the same mechanical linkage, subject to a non-holonomic constraint, such as a skate contact, can exhibit either static buckling or flutter instability. Chapter 6 moves away from neural control and studies the shape of animal bodies and their relationship to stability in locomotion. We investigate why small land animals tend to have a crouched or sprawled posture, whereas larger animals are generally more upright. We propose a new hypothesis that the scaling of body aspect ratio with size is driven by the scale-dependent unevenness of natural terrain. We show that the scaling law arising from the need for stability on rough natural terrain correctly predicts the frontal aspect ratio scaling law across 335 terrestrial vertebrates and invertebrates, spanning eight orders of magnitude in mass so that smaller animals have a wider aspect ratio. We also carry out statistical analyses that consider the phylogenetic relationship among the species in our dataset to show that the scaling is not due to gradual changes of the traits over time. Thus, stability demands on natural terrain may have driven the macroevolution of body aspect ratio across terrestrial animals. Interrogating unstable and marginally stable behaviors has helped us identify the morphological and control features that allow animals to perform robustly in noisy environments where perfect sensory feedback cannot be assumed. Although the thesis identifies the `what\u27 and `why,\u27 further studies are needed to understand `how\u27 mechanics and development intertwine to give rise to control and form in growing and adapting biological organisms

A data-driven neuromuscular model of walking and its application to prosthesis control

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 119-123).In this thesis we present a data-driven neuromuscular model of human walking and its application to prosthesis control. The model is novel in that it leverages tendon elasticity to more accurately predict the metabolic consumption of walking than conventional models. Paired with a reflex-based neural drive the model has been applied in the control of a robotic ankle-foot prosthesis, producing speed adaptive behavior. Current neuromuscular models significantly overestimate the metabolic demands of walking. We believe this is because they do not adequately consider the role of elasticity; specifically the parameters that govern the force-length relations of tendons in these models are typically taken from published values determined from cadaver studies. To investigate this issue we first collected kinematic, kinetic, electromyographic (EMG), and metabolic data from five subjects walking at six different speeds. The kinematic and kinetic data were used to estimate muscle lengths, muscle moment arms, and joint moments while the EMG data were used to estimate muscle activations. For each subject we performed a kinematically clamped optimization, varying the parameters that govern the force-length curve of each tendon while simultaneously seeking to minimize metabolic cost and maximize agreement with the observed joint moments. We found a family of parameter sets that excel at both objectives, providing agreement with both the collected kinetic and metabolic data. This identification allows us to accurately predict the metabolic cost of walking as well as the force and state of individual muscles, lending insight into the roles and control objectives of different muscles throughout the gait cycle. This optimized muscle-tendon morphology was then applied with an optimized linear reflex architecture in the control of a powered ankle-foot prosthesis. Specifically, the model was fed the robot's angle and state and used to command output torque. Clinical trials were conducted that demonstrated speed adaptive behavior; commanded net work was seen to increase with walking speed. This result supports both the efficacy of the modeling approach and its potential utility in controlling life-like prosthetic limbs.by Jared Markowitz.Ph.D

Analysis of complex movements

In most everyday repetitive movements such as walking, sitting, and reaching, humans exhibit large degrees of regularity. However, at the other end of the movement spectrum, in complex movement tasks, such as retrieving an object from a cluttered environment or choosing balance positions for transporting a large, unwieldy object, humans are inventive problem solvers. Therefore, in the quest to understand the human movement system, it would be essential to know if general movements have regularities across subjects as it would provide an essential scaffold in the development of more detailed dynamic movement models. This research mainly aims to learn the principles behind large-scale arbitrary movements, particularly regarding variations between different subjects. For example, given a goal-directed task, do the movements appear similar across subjects, or are movements very individualized? The tasks for the research covers developing an interactive virtual reality environment to capture goal-directed whole-body human movements, getting insights into the regularities underlying those motion capture data (kinematics), and finally analyzing the corresponding energy cost by using a forty-eight degree of freedom dynamic human model (dynamics). The results illustrate that humans chose trajectories that are economical in energetic cost while accomplishing goal-directed tasks.Computer Science

A multilevel framework to measure, model, promote, and enhance the symbiotic cooperation between humans and robotic devices

In the latest decades, the common perception about the role of robotic devices in the modern society dramatically changed. In the early stages of robotics, temporally located in the years of the economic boom, the development of new devices was driven by the industrial need of producing more while reducing production time and costs. The demand was, therefore, for robotic devices capable of substituting the humans in performing simple and repetitive activities. The execution of predefined basic activities in the shortest amount of time, inside carefully engineered and confined environments, was the mission of robotic devices. Beside the results obtained in the industrial sector, a progressive widening of the fields interested in robotics – such as rehabilitation, elderly care, and medicine – led to the current vision of the device role. Indeed, these challenging fields require the robot to be a partner, which works side-by-side with the human. Therefore, the device needs to be capable of actively and efficiently interacting with humans, to provide support and overcome their limits in the execution of shared activities, even in highly unpredictable everyday environments. Highly complex and advanced robots, such as surgical robots, rehabilitation devices, flexible manipulators, and service and companion robots, have been recently introduced into the market; despite their complexity, however, they are still tools to be used to perform, better or faster, very specific tasks. The current open challenge is, therefore, to develop a new generation of symbiotically cooperative robotic partners, adding to the devices the capability to detect, understand, and adapt to the real intentions, capabilities, and needs of the humans. To achieve this goal, a bidirectional information channel shall be built to connect the human and the device. In one direction, the device requires to be informed about the state of its user; in the other direction, the human needs to be informed about the state of the whole interacting system. This work reports the research activities that I conducted during my PhD studies in this research direction. Those activities led to the design, development, and assessment on a real application of an innovative multilevel framework to close the cooperation loop between a human and a robotic device, thus promoting and enhancing their symbiotic interaction. Three main levels have been identified as core elements to close this loop: the measure level, the model level, and the extract/synthesize level. The former aims at collecting experimental measures from the whole interacting system; the second aims at estimating and predicting its dynamic behavior; the last aims at providing quantitative information to both the human and the device about their performances and about how to modify their behavior to improve their interaction symbiosis. Within the measure level, the focus has been concentrated on investigating, critically comparing, and selecting the most suitable and advanced technologies to measure kinematics and dynamics quantities in a portable and minimally intrusive way. Particular attention has been paid to new emerging technologies; moreover, useful protocols and pipelines already recognized as de-facto in other fields have been successfully adapted to fit the needs of the man-machine interaction context. Finally, the design of a new sensor has been started to overcome the lack of tools capable of effectively measuring human-device interaction forces. To implement the model level, a common platform to perform integrated multilevel simulations – i.e. simulations where the device and the human are considered together as interacting entities – has been selected and extensively validated. Furthermore, critical aspects characterizing the modeling of the device, the human, and their interactions have been studied and possible solutions have been proposed. For example, modeling the mechanics and the control within the selected software platform allowed accurate estimations of their behavior. To estimate human behavior, new methodologies and approaches based on anatomical neuromusculoskeletal models have been developed, validated, and released as open-source tools for the community, to allow accurate estimates of both kinematics and dynamics at run-time – i.e. at the same time that the movements are performed. An inverse kinematics approach has been developed and validated to estimate human joint angles from the orientation measurements provided by wearable inertial systems. Additionally, a state of the art neuromusculoskeletal modeling toolbox has been improved and interfaced with the other tools of the multilevel framework, to accurately predict human muscle forces, joint moments, and muscle and joint stiffness from electromyographic and kinematic measures. To estimate and predict the interactions, contact models, parameters optimization procedures, and high-level cooperation strategies have been investigated, developed, and applied. Within the extract/synthesize level, the information provided by the other levels has been combined together to develop informative feedbacks for both the device and the human. In one direction, the device has been provided with control signals defining how to adjust the provided support to comply with the task goals and with the human current capabilities and needs. In the other direction, quantitative feedbacks have been developed to inform the human about task execution performances, task targets, and support provided by the device. This information has been provided to the user as visual feedbacks designed to be both exhaustively informative and minimally distractive, to prevent possible loss of focus. Moreover, additional feedbacks have been devised to help external observers – therapists in the rehabilitation contexts or task planners and ergonomists in the industrial field – in the design and refinement of effective personalized tasks and long-term goals. The integration of all the hardware and software tools of each level in a modular, flexible, and reliable software framework, based on a well known robotic middleware, has been fundamental to handle the communication and information exchange processes. The developed general framework has been finally specialized to face the specific needs of robotic-aided gait rehabilitation. In this context, indeed, the final aim of promoting the symbiotic cooperation is translatable in maximizing treatment effectiveness for the patients by actively supporting their changing needs and capabilities while keeping them engaged during the whole rehabilitation process. The proposed multilevel framework specialization has been successfully used, as valuable answer to those needs, within the context of the Biomot European project. It has been, indeed, fundamental to face the challenges of closing the informative loop between the user and the device, and providing valuable quantitative information to the external observers. Within this research project, we developed an innovative compliant wearable exoskeleton prototype for gait rehabilitation capable of adjusting, at run-time, the provided support according to different cooperation strategies and to user needs and capabilities. At the same time, the wearer is also engaged in the rehabilitation process by intuitive visual feedbacks about his performances in the achievement of the rehabilitation targets and about the exoskeleton support. Both researchers and clinical experts evaluating the final rehabilitation application of the multilevel framework provided enthusiastic feedbacks about the proposed solutions and the obtained results. To conclude, the modular and generic multilevel framework developed in this thesis has the potential to push forward the current state of the art in the applications where a symbiotic cooperation between robotic devices and humans is required. Indeed, it effectively endorses the development of a new generation of robotic devices capable to perform challenging cooperative tasks in highly unpredictable environments while complying with the current needs, intentions, and capabilities of the human