An artificial neural network that utilizes hip joint actuations to control bifurcations and chaos in a passive dynamic bipedal walking model

Chaos is a central feature of human locomotion and has been suggested to be a window to the control mechanisms of locomotion. In this investigation, we explored how the principles of chaos can be used to control locomotion with a passive dynamic bipedal walking model that has a chaotic gait pattern. Our control scheme was based on the scientific evidence that slight perturbations to the unstable manifolds of points in a chaotic system will promote the transition to new stable behaviors embedded in the rich chaotic attractor. Here we demonstrate that hip joint actuations during the swing phase can provide such perturbations for the control of bifurcations and chaos in a locomotive pattern. Our simulations indicated that systematic alterations of the hip joint actuations resulted in rapid transitions to any stable locomotive pattern available in the chaotic locomotive attractor. Based on these insights, we further explored the benefits of having a chaotic gait with a biologically inspired artificial neural network (ANN) that employed this chaotic control scheme. Remarkably, the ANN was quite robust and capable of selecting a hip joint actuation that rapidly transitioned the passive dynamic bipedal model to a stable gait embedded in the chaotic attractor. Additionally, the ANN was capable of using hip joint actuations to accommodate unstable environments and to overcome unforeseen perturbations. Our simulations provide insight on the advantage of having a chaotic locomotive system and provide evidence as to how chaos can be used as an advantageous control scheme for the nervous system

A New Chaotic System with Line of Equilibria: Dynamics, Passive Control and Circuit Design

A new chaotic system with line equilibrium is introduced in this paper. This system consists of five terms with two transcendental nonlinearities and two quadratic nonlinearities. Various tools of dynamical system such as phase portraits, Lyapunov exponents, Kaplan-Yorke dimension, bifurcation diagram and Poincarè map are used. It is interesting that this system has a line of fixed points and can display chaotic attractors. Next, this paper discusses control using passive control method. One example is given to insure the theoretical analysis. Finally, for the  new chaotic system, An electronic circuit for realizing the chaotic system has been implemented. The numerical simulation by using MATLAB 2010 and implementation of circuit simulations by using MultiSIM 10.0 have been performed in this study

In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

Novel approach for representing, generalising, and quantifying periodic gaits

Our goal is to introduce a novel method for representing, generalising, and comparing gaits; particularly, walking gait. Human walking gaits are a result of complex, interdependent factors that include variations resulting from embodiments, environment and tasks, making techniques that use average template frameworks suboptimal for systematic analysis or corrective interventions. The proposed work aims to devise methodologies for being able to represent gaits and gait transitions such that optimal policies that eliminate the inter-personal variations from tasks and embodiment may be recovered. Our approach is built upon (i) work in the domain of null-space policy recovery and (ii) previous work in generalisation for point-to-point movements. The problem is formalised using a walking phase model, and the null-space learning method is used to generalise a consistent policy from multiple observations with rich variations. Once recovered, the underlying policies (mapped to different gait phases) can serve as reference guideline to quantify and identify pathological gaits while being robust against interpersonal and task variations. To validate our methods, we have demonstrated robustness of our method with simulated sagittal 2-link gait data with multiple ground truth constraints and policies. Pathological gait identification was then tested on real-world human gait data with induced gait abnormality, with the proposed method showing significant robustness to variations in speed and embodiment compared to template based methods. Future work will extend this to kinetic features and higher degree-of-freedom

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Embodied Cognition and Sport

Successful athletic performance requires precision in many respects. A batter stands behind home plate awaiting the arrival of a ball that is less than three inches in diameter and moving close to 100 mph. His goal is to hit it with a ba­­t that is also less than three inches in diameter. This impressive feat requires extraordinary temporal and spatial coordination. The sweet spot of the bat must be at the same place, at the same time, as the ball. A basketball player must keep a ball bouncing as she speeds from one end of the court to another, evading defensive players. She may never break pace as she lifts from the ground, throwing the ball fifteen feet toward a hoop that is eighteen inches in diameter. One task facing a psychologist involves explaining how the body does such things within the sometimes very demanding spatial and temporal constraints that a given task imposes. Part of the goal of this chapter is to sketch the commitments of an embodied approach to such an explanation. We shall see that an embodied account of motor skills draws concepts that depart radically from more traditional cognitivist theories of motor activity. Similarly, because an embodied approach to cognition introduces new ways to understand the human capacity for social interaction, it also promises to shed new light on how athletes coordinate their actions with each other

Human Movement Variability, Nonlinear Dynamics, and Pathology: Is There A Connection?

Fields studying movement generation, including robotics, psychology, cognitive science, and neuroscience utilize concepts and tools related to the pervasiveness of variability in biological systems. The concept of variability and the measures for nonlinear dynamics used to evaluate this concept open new vistas for research in movement dysfunction of many types. This review describes innovations in the exploration of variability and their potential importance in understanding human movement. Far from being a source of error, evidence supports the presence of an optimal state of variability for healthy and functional movement. This variability has a particular organization and is characterized by a chaotic structure. Deviations from this state can lead to biological systems that are either overly rigid and robotic or noisy and unstable. Both situations result in systems that are less adaptable to perturbations, such as those associated with unhealthy pathological states or absence of skillfulness