59 research outputs found
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Bio-inspired friction switches: adaptive pulley systems
Frictional influences in tendon-driven robotic systems are generally
unwanted, with efforts towards minimizing them where possible. In the human
hand however, the tendon-pulley system is found to be frictional with a
difference between high-loaded static post-eccentric and post-concentric force
production of 9-12% of the total output force. This difference can be directly
attributed to tendon-pulley friction. Exploiting this phenomenon for robotic
and prosthetic applications we can achieve a reduction of actuator size, weight
and consequently energy consumption. In this study, we present the design of a
bio-inspired friction switch. The adaptive pulley is designed to minimize the
influence of frictional forces under low and medium-loading conditions and
maximize it under high-loading conditions. This is achieved with a
dual-material system that consists of a high-friction silicone substrate and
low-friction polished steel pins. The system, designed to switch its frictional
properties between the low-loaded and high-loaded conditions, is described and
its behavior experimentally validated with respect to the number and spacing of
pins. The results validate its intended behavior, making it a viable choice for
robotic tendon-driven systems.Comment: Conference. First submission, before review
Memristor models for machine learning
In the quest for alternatives to traditional CMOS, it is being suggested that
digital computing efficiency and power can be improved by matching the
precision to the application. Many applications do not need the high precision
that is being used today. In particular, large gains in area- and power
efficiency could be achieved by dedicated analog realizations of approximate
computing engines. In this work, we explore the use of memristor networks for
analog approximate computation, based on a machine learning framework called
reservoir computing. Most experimental investigations on the dynamics of
memristors focus on their nonvolatile behavior. Hence, the volatility that is
present in the developed technologies is usually unwanted and it is not
included in simulation models. In contrast, in reservoir computing, volatility
is not only desirable but necessary. Therefore, in this work, we propose two
different ways to incorporate it into memristor simulation models. The first is
an extension of Strukov's model and the second is an equivalent Wiener model
approximation. We analyze and compare the dynamical properties of these models
and discuss their implications for the memory and the nonlinear processing
capacity of memristor networks. Our results indicate that device variability,
increasingly causing problems in traditional computer design, is an asset in
the context of reservoir computing. We conclude that, although both models
could lead to useful memristor based reservoir computing systems, their
computational performance will differ. Therefore, experimental modeling
research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio
Magneto-mechanical actuation model for fin-based locomotion
In this paper, we report the results from the analysis of a numerical model
used for the design of a magnetic linear actuator with applications to
fin-based locomotion. Most of the current robotic fish generate bending motion
using rotary motors which implies at least one mechanical conversion of the
motion. We seek a solution that directly bends the fin and, at the same time,
is able to exploit the magneto-mechanical properties of the fin material. This
strong fin-actuator coupling blends the actuator and the body of the robot,
allowing cross optimization of the system's elements.
We study a simplified model of an elastic element, a spring-mass system
representing a flexible fin, subjected to nonlinear forcing, emulating magnetic
interaction. The dynamics of the system is studied under unforced and periodic
forcing conditions. The analysis is focused on the limit cycles present in the
system, which allows the periodic bending of the fin and the generation of
thrust. The frequency, maximum amplitude and center of the periodic orbits
(offset of the bending) depend directly on the stiffness of the fin and the
intensity of the forcing; we use this dependency to sketch a simple parameter
controller. Although the model is strongly simplified, it provides means to
estimate first values of the parameters for this kind of actuator and it is
useful to evaluate the feasibility of minimal actuation control of such
systems.Comment: Conference paper, 201
Robustness: a new SLIP model based criterion for gait transitions in bipedal locomotion
Bipedal locomotion is a phenomenon that still eludes a fundamental and
concise mathematical understanding. Conceptual models that capture some
relevant aspects of the process exist but their full explanatory power is not
yet exhausted. In the current study, we introduce the robustness criterion
which defines the conditions for stable locomotion when steps are taken with
imprecise angle of attack. Intuitively, the necessity of a higher precision
indicates the difficulty to continue moving with a given gait. We show that the
spring-loaded inverted pendulum model, under the robustness criterion, is
consistent with previously reported findings on attentional demand during human
locomotion. This criterion allows transitions between running and walking, many
of which conserve forward speed. Simulations of transitions predict Froude
numbers below the ones observed in humans, nevertheless the model
satisfactorily reproduces several biomechanical indicators such as hip
excursion, gait duty factor and vertical ground reaction force profiles.
Furthermore, we identify reversible robust walk-run transitions, which allow
the system to execute a robust version of the hopping gait. These findings
foster the spring-loaded inverted pendulum model as the unifying framework for
the understanding of bipedal locomotion.Comment: unpublished, in preparatio
A computational analysis of motor synergies by dynamic response decomposition
Analyses of experimental data acquired from humans and other vertebrates have suggested that motor commands may emerge from the combination of a limited set of modules. While many studies have focused on physiological aspects of this modularity, in this paper we propose an investigation of its theoretical foundations. We consider the problem of controlling a planar kinematic chain, and we restrict the admissible actuations to linear combinations of a small set of torque profiles (i.e. motor synergies). This scheme is equivalent to the time-varying synergy model, and it is formalized by means of the dynamic response decomposition (DRD). DRD is a general method to generate open-loop controllers for a dynamical system to solve desired tasks, and it can also be used to synthesize effective motor synergies. We show that a control architecture based on synergies can greatly reduce the dimensionality of the control problem, while keeping a good performance level. Our results suggest that in order to realize an effective and low-dimensional controller, synergies should embed features of both the desired tasks and the system dynamics. These characteristics can be achieved by defining synergies as solutions to a representative set of task instances. The required number of synergies increases with the complexity of the desired tasks. However, a possible strategy to keep the number of synergies low is to construct solutions to complex tasks by concatenating synergy-based actuations associated to simple point-to-point movements, with a limited loss of performance. Ultimately, this work supports the feasibility of controlling a non-linear dynamical systems by linear combinations of basic actuations, and illustrates the fundamental relationship between synergies, desired tasks and system dynamics
Synthesis and Adaptation of Effective Motor Synergies for the Solution of Reaching Tasks
Taking inspiration from the hypothesis of muscle synergies, we propose a
method to generate open loop controllers for an agent solving point-to-point
reaching tasks. The controller output is defined as a linear combination of a
small set of predefined actuations, termed synergies. The method can be
interpreted from a developmental perspective, since it allows the agent to
autonomously synthesize and adapt an effective set of synergies to new
behavioral needs. This scheme greatly reduces the dimensionality of the control
problem, while keeping a good performance level. The framework is evaluated in
a planar kinematic chain, and the quality of the solutions is quantified in
several scenarios.Comment: conference pape
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