Juan Pablo Carbajal

Varying body stiffness for aquatic locomotion

Fish excel in their swimming capabilities. These result from a dynamic interplay of actuation, passive properties of fish body, and interaction with the surrounding fluid. In particular, fish are able to exploit wakes that are generated by objects in flowing water. A powerful demonstration that this is largely due to passive body properties are studies on dead trout. Inspired by that, we developed a multi joint swimming platform that explores the potential of a passive dynamic mechanism. The platform has one actuated joint only, followed by three passive joints whose stiffness can be changed online, individually, and can be set to an almost arbitrary nonlinear stiffness profile. In a set of experiments, using online optimization, we investigated how the platform can discover optimal stiffness distribution along its body in response to different frequency and amplitude of actuation. We show that a heterogeneous stiffness distribution - each joint having a different value - outperforms a homogeneous one in producing thrust. Furthermore, different gaits emerged in different settings of the actuated joint. This work illustrates the potential of online adaption of passive body properties, leading to optimized swimming, especially in an unsteady environment.

Memristor models for machine learning

In the quest for alternatives to traditional complementary metal-oxide-semiconductor, 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 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.

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.

Exploiting the passive dynamics of a compliant leg to develop gait transitions.

In the area of bipedal locomotion, the spring-loaded inverted pendulum model has been proposed as a unified framework to explain the dynamics of a wide variety of gaits. In this paper, we present an analysis of the mathematical model and its dynamical properties. We use the perspective of hybrid dynamical systems to study the dynamics and define concepts such as partial stability and viability. With this approach, on the one hand, we identify stable and unstable regions of locomotion. On the other hand, we find ways to exploit the unstable regions of locomotion to induce gait transitions at a constant energy regime. Additionally, we show that simple nonconstant angle of attack control policies can render the system almost always stable.

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.

From walking to running a natural transition in the SLIP model using the hopping gait

In this paper we adopt the spring loaded inverted pendulum (SLIP) model as the mathematical framework to represent biped locomotion, but in contrast with previous studies, we redefine the conditions for valid locomotion. As a consequence we identify new ways to produce gait transitions (e.g. change from walking to running) through the control of the angle of attack, at a constant energy level. Moreover, we show that the new valid conditions of locomotion allow the representation of the hopping gait. This new gait requires two different angles of attack for its execution, hence constant angle of attack policies are not applicable. First, we show the regions of phase space where one step gait transitions exist. Next, we report the region where it is possible to generate a periodic hopping gait. Mainly, the two results imply that through the control of the angle of attack the system can exploit its passive dynamics to induce transitions between running, walking and hooping or keep the system stable in any of these gaits. Finally, we briefly discuss the relation between these findings and the use of complaints legs in robots.

Scaling Laws in Robotics

Scaling laws are pervasive in biological systems, found in a large number of life processes, and across 27 orders of magnitude. Recent findings show both biological and engineered motors adhering to two fundamental regimes for the mass scaling of maximum force output. This scaling law is of particular interest for the robotics field as it can affect the design stage of a robot. In this study we present data of motors commonly used in robotic applications and find an adherence to a similar power law of mass scaling of maximum torque output in two groups, group a, (Ga ∝ m1.00) and group b (Gb ∝ m1.27). Findings imply that there could exist an upper motor limit of maximum specific torque/force that should be taken under consideration in robot design. Additionally, we show how a robots minimum mass can be calculated with motor mass being the only necessary parameter.

Learning a Curvature Dynamic Model of an Octopus-inspired Soft Robot Arm Using Flexure Sensors☆

This work presents a novel technique for sensing body dynamics for a soft-robot arm inspired by the octopus, using flexure sensors. The aim is to develop a sensing technique which can also simultaneously enable learning of the body dynamics that can then be used for control. Flexure sensing is advantageous for a soft bodied robot since it is a direct measure of local behaviour along the arm, and is closely connected with the piecewise constant curvature assumption employed for such robots. Initial results on simulated sensor measurements and dynamics learning are presented and ongoing work and applications are discussed.

Burrowing behaviour of robotic bivalves with synthetic morphologies.

Several bivalve species burrow into sandy sediments to reach their living position. There are many hypotheses concerning the functional morphology of the bivalve shell for burrowing. Observational studies are limited and often qualitative and should be complemented by a synthetic approach mimicking the burrowing process using a robotic emulation. In this paper we present a simple mechatronic set-up to mimic the burrowing behaviour of bivalves. As environment we used water and quartz sand contained in a glass tank. Bivalve shells were mathematically modelled on the computer and then materialized using a 3D printer. The burrowing motion of the shells was induced by two external linear motors. Preliminary experiments did not expose any artefacts introduced to the burrowing process by the set-up. We tested effects of shell size, shape and surface sculpturing on the burrowing performance. Neither the typical bivalve shape nor surface sculpture did have a clear positive effect on burrowing depth in the performed experiments. We argue that the presented method is a valid and promising approach to investigate the functional morphology of bivalve shells and should be improved and extended in future studies. In contrast to the observation of living bivalves, our approach offers complete control over the parameters defining shell morphology and motion pattern. The technical set-up allows the systematic variation of all parameters to quantify their effects. The major drawback of the built set-up was that the reliability and significance of the results was limited by the lack of an optimal technique to standardize the sediment state before experiments.

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 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.