The role of feedback in morphological computation with compliant bodies

The generation of robust periodic movements of complex nonlinear robotic systems is inherently difficult, especially, if parts of the robots are compliant. It has previously been proposed that complex nonlinear features of a robot, similarly as in biological organisms, might possibly facilitate its control. This bold hypothesis, commonly referred to as morphological computation, has recently received some theoretical support by Hauser etal. (Biol Cybern 105:355-370, doi: 10.1007/s00422-012-0471-0 , 2012). We show in this article that this theoretical support can be extended to cover not only the case of fading memory responses to external signals, but also the essential case of autonomous generation of adaptive periodic patterns, as, e.g., needed for locomotion. The theory predicts that feedback into the morphological computing system is necessary and sufficient for such tasks, for which a fading memory is insufficient. We demonstrate the viability of this theoretical analysis through computer simulations of complex nonlinear mass-spring systems that are trained to generate a large diversity of periodic movements by adapting the weights of a simple linear feedback device. Hence, the results of this article substantially enlarge the theoretically tractable application domain of morphological computation in robotics, and also provide new paradigms for understanding control principles of biological organism

Morphological properties of mass-spring networks for optimal locomotion learning

Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size

Exploiting short-term memory in soft body dynamics as a computational resource

Soft materials are not only highly deformable but they also possess rich and diverse body dynamics. Soft body dynamics exhibit a variety of properties, including nonlinearity, elasticity, and potentially infinitely many degrees of freedom. Here we demonstrate that such soft body dynamics can be employed to conduct certain types of computation. Using body dynamics generated from a soft silicone arm, we show that they can be exploited to emulate functions that require memory and to embed robust closed-loop control into the arm. Our results suggest that soft body dynamics have a short-term memory and can serve as a computational resource. This finding paves the way toward exploiting passive body dynamics for control of a large class of underactuated systems.Comment: 22 pages, 11 figures; email address correcte

On developing theory of reservoir computing for sensing applications: the state weaving environment echo tracker (SWEET) algorithm

As a paradigm of computation, reservoir computing has gained an enormous momentum. In principle, any sufficiently complex dynamical system equipped with a readout layer can be used for any computation. This can be achieved by only adjusting the readout layer. Owning to this inherent flexibility of implementation, new applications of reservoir computing are being reported at a constant rate. However, relatively few studies focus on sensing, and in the ones that do, the reservoir is often exploited in a somewhat passive manner. The reservoir is used to post-process the signal from sensing elements that are placed separately, and the reservoir could be replaced by other information processing system without loss of functionality of the sensor (\u27reservoir computing and sensing\u27). An entirely different novel class of sensing approaches is being suggested, to be referred to as \u27reservoir computing for sensing\u27, where the reservoir plays a central role. In the State Weaving Environment Echo Tracker (SWEET) sensing approach, the reservoir functions as the sensing element if the dynamical states of the reservoir and the environment one wishes to analyze are strongly interwoven. Some distinct characteristics of reservoir computing (in particular the separability and the echo state properties) are carefully exploited to achieve sensing functionality. The SWEET approach is formulated both as a generic device setup, and as an abstract mathematical algorithm. This algorithmic template could be used to develop a theory (or a class of theories) of \u27reservoir computing for sensing\u27, which could provide guidelines for engineering novel sensing applications. It could also provide ideas for a creative recycling of the existing sensing solutions. For example, the Horizon 2020 project RECORD-IT (Reservoir Computing with Real-time Data for future IT) exploits the SWEET sensing algorithm for ion detection. Accordingly, the terms SWEET sensing algorithm and the RECORD-IT sensing algorithm can be used interchangeably