Design and control of compliant tensegrity robots through simulation and hardware validation

2-dimensional impact-damping electrostatic actuators with elastomer-enhanced auxetic structure

Biomimetic robots yearn for compliant actuators that are comparable to biological muscle in both functions and structural properties. For that, electrostatic actuators have been developed to imitate bio-muscle in features of fast response, high power, energy-efficiency, etc. However, those actuators typically lack impact damping performance, making them vulnerable and unstable in real applications. Here, we present auxetic electrostatic actuators that address this issue and demonstrate muscle-like performance by using elastomer-enhanced auxetics and electrostatic zipping mechanism. The proposed actuators contract linearly on applied voltage, producing large actuation strength (15 N) and contraction ratio (59%). Fabricated from readily available materials, our prototypes can quickly attenuate vibrations caused by impacts and absorb shock energy in 0.3 s. Furthermore, leveraging their 2-dimensional working mode and self-locking mechanism, a stiffness-changing muscle for a robotic arm and an active tensegrity device exemplify the potential applications of auxetic electrostatic actuators to a wide range of bionic robots.

A Multimodal Self-Propelling Tensegrity Structure

Tensegrity structure is composed of tensile cables and compressive rods, offering high stiffness-to-mass ratio, deploy ability, and excellent energy damping capability. The active and dynamic tensegrity designs demonstrate great potential for soft robots. In previous designs, the movement has relied on carefully controlled input power or manually controlled light irradiation, limiting their potential applications. Here, a hybrid tensegrity structure (HTS) is constructed by integrating thermally responsive cables, nonresponsive cables, and stiff rods. The HTS can self-propel continuously on a hot surface due to its unique geometry. The HTS allows for the easy achievement of multimodal self-propelled locomotive modes, which has been challenging for previously demonstrated self-propelling structures. Additionally, using Velcro tapes to adhere the rods and cables together, a modulable and reassemblable HTS is created. The HTS introduced in this study presents a new strategy and offers a large design space for constructing self-propelling and modulable robots.

Self-learning physical reservoir computer

A self-learning physical reservoir computer is demonstrated using an adaptive oscillator. Whereas physical reservoir computing repurposes the dynamics of a physical system for computation through machine learning, adaptive oscillators can innately learn and store information in plastic dynamic states. The adaptive state(s) can be used directly as physical node(s), but these plastic states can also be used to self-learn the optimal reservoir parameters for more complex tasks requiring virtual nodes from the base oscillator. Both this self-learning property for reconfigurable computing and the morphable logic gate property of the adaptive oscillator make this an ideal candidate for a multipurpose neuromorphic processor.

Reservoir computing with higher-order interactive coupled pendulums

The reservoir computing approach utilizes a time series of measurements as input to a high-dimensional dynamical system known as a reservoir. However, the approach relies on sampling a random matrix to define its underlying reservoir layer, which leads to numerous hyperparameters that need to be optimized. Here, we propose a nonlocally coupled pendulum model with higher-order interactions as a novel reservoir, which requires no random underlying matrices and fewer hyperparameters. We use Bayesian optimization to explore the hyperparameter space within a minimal number of iterations and train the coupled pendulums model to reproduce the chaotic attractors, which simplifies complicated hyperparameter optimization. We illustrate the effectiveness of our technique with the Lorenz system and the Hindmarsh-Rose neuronal model, and we calculate the Pearson correlation coefficients between time series and the Hausdorff metrics in the phase space. We demonstrate the contribution of higher-order interactions by analyzing the interaction between different reservoir configurations and prediction performance, as well as computations of the largest Lyapunov exponents. The chimera state is found as the most effective dynamical regime for prediction. The findings, where we present a new reservoir structure, offer potential applications in the design of high-performance modeling of dynamics in physical systems.

Handwritten digit recognition by spin waves in a Skyrmion reservoir

By performing numerical simulations for the handwritten digit recognition task, we demonstrate that a magnetic skyrmion lattice confined in a thin-plate magnet possesses high capability of reservoir computing. We obtain a high recognition rate of more than 88%, higher by about 10% than a baseline taken as the echo state network model. We find that this excellent performance arises from enhanced nonlinearity in the transformation which maps the input data onto an information space with higher dimensions, carried by interferences of spin waves in the skyrmion lattice. Because the skyrmions require only application of static magnetic field instead of nanofabrication for their creation in contrast to other spintronics reservoirs, our result consolidates the high potential of skyrmions for application to reservoir computing devices.

The morphological paradigm in robotics

In the paper, we are going to show how robotics is undergoing a shift in a bionic direction after a period of emphasis on artificial intelligence and increasing computational efficiency, which included isolation and extreme specialization. We assemble these new developments under the label of the morphological paradigm. The change in its paradigms and the development of alternatives to the principles that dominated robotics for a long time contains a more general epistemological significance. The role of body, material, environment, interaction and the paradigmatic status of biological and evolutionary systems for the principles of control are crucial here. Our focus will be on the introduction of the morphological paradigm in a new type of robotics and to contrast the interests behind this development with the interests shaping former models. The article aims to give a clear account of the changes in principles of orientation and control as well as concluding general observation in terms of historical epistemology, suggesting further political-epistemological analysis.

From tensegrity to human organs-on-chips: implications for mechanobiology and mechanotherapeutics

The field of mechanobiology, which focuses on the key role that physical forces play in control of biological systems, has grown enormously over the past few decades. Here, I provide a brief personal perspective on the development of the tensegrity theory that contributed to the emergence of the mechanobiology field, the key role that crossing disciplines has played in its development, and how it has matured over time. I also describe how pursuing questions relating to mechanochemical transduction and mechanoregulation can lead to the creation of novel technologies and open paths for development of new therapeutic strategies for a broad range of diseases and disorders.

Mechanical neural networks: Architected materials that learn behaviors

Aside from some living tissues, few materials can autonomously learn to exhibit desired behaviors as a consequence of prolonged exposure to unanticipated ambient loading scenarios. Still fewer materials can continue to exhibit previously learned behaviors in the midst of changing conditions (e.g., rising levels of internal damage, varying fixturing scenarios, and fluctuating external loads) while also acquiring new behaviors best suited for the situation at hand. Here, we describe a class of architected materials, called mechanical neural networks (MNNs), that achieve such learning capabilities by tuning the stiffness of their constituent beams similar to how artificial neural networks (ANNs) tune their weights. An example lattice was fabricated to demonstrate its ability to learn multiple mechanical behaviors simultaneously, and a study was conducted to determine the effect of lattice size, packing configuration, algorithm type, behavior number, and linear-versus-nonlinear stiffness tunability on MNN learning as proposed. Thus, this work lays the foundation for artificial-intelligent (AI) materials that can learn behaviors and properties.

Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone

This paper proposes a method for simultaneously planning a path and a sequence of deformations for a tensegrity drone. Previous work in the field required the use of bounding surfaces, making the planning more conservative. The proposed method takes advantage of the need to use mixed-integer variables in choosing the drone path (using big-M relaxation) to simultaneously choose the configuration of the drone, eliminating the need to use semidefinite matrices to encode configurations, as was done previously. The numerical properties of the algorithm are demonstrated in numerical studies. To show the viability of tensegrity drones, the first tensegrity quadrotor Tensodrone was build. The Tensodrone is based on a six-bar tensegrity structure that is inherently compliant and can withstand crash landings and frontal collisions with obstacles. This makes the robot safe for the humans around it and protects the drone itself during aggressive maneuvers in constrained and cluttered environments, a feature that is becoming increasingly important for challenging applications that include cave exploration and indoor disaster response.

Durable Pneumatic Artificial Muscles with Electric Conductivity for Reliable Physical Reservoir Computing

A McKibben-type pneumatic artificial muscle (PAM) is a soft actuator that is widely used in soft robotics, and it generally exhibits complex material dynamics with nonlinearity and hysteresis. In this letter, we propose an extremely durable PAM containing carbon black aggregates and show that its dynamics can be used as a computational resource based on the framework of physical reservoir computing (PRC). By monitoring the information processing capacity of our PAM, we verified that its computational performance will not degrade even if it is randomly actuated more than one million times, which indicates extreme durability. Furthermore, we demonstrate that the sensing function can be outsourced to the soft material dynamics itself without external sensors based on the framework of PRC. Our study paves the way toward reliable information processing powered by soft material dynamics.

Local Discrimination Based on Piezoelectric Sensing in Robots Composed of Soft Matter with Different Physical Properties

The coronavirus epidemic has attracted significant attention to the applications of pet robots which can be used to treat and entertain people in their homes. However, pet robots are fabricated using hard materials and it is difficult for them to communicate with people through contact. Soft robots are expected to realize communication through contact similar to that of actual pets. Soft robots provide people with a sense of healing and security owing to their softness and can extract rich information through external stimuli by applying a machine learning framework called physical-reservoir computing. It is crucial to determine the differences between the physical properties of soft materials that affect the information extracted from a soft body to develop an intelligent soft robot. In this study, two owl-shaped soft robots with different softnesses were developed to analyze the characteristics of the signal data obtained via piezoelectric film sensors embedded in models with different physical properties. An accuracy of 94.2% and 95.9% was obtained for touched part classification using 1D CNN and logistic regression models, respectively. Additionally, the relationship between the softness of material and classification performance was investigated by comparing the distribution of part classification accuracy for different hyper-parameters of two owl models.

Designing Expandable-Structure Robots for Human-Robot Interaction

In this paper, we survey the emerging design space of expandable structures in robotics, with a focus on how such structures may improve human-robot interactions. We detail various implementation considerations for researchers seeking to integrate such structures in their own work and describe how expandable structures may lead to novel forms of interaction for a variety of different robots and applications, including structures that enable robots to alter their form to augment or gain entirely new capabilities, such as enhancing manipulation or navigation, structures that improve robot safety, structures that enable new forms of communication, and structures for robot swarms that enable the swarm to change shape both individually and collectively. To illustrate how these considerations may be operationalized, we also present three case studies from our own research in expandable structure robots, sharing our design process and our findings regarding how such structures enable robots to produce novel behaviors that may capture human attention, convey information, mimic emotion, and provide new types of dynamic affordances.

Dynamic effects on reservoir computing with a Hopf oscillator

Limit cycle oscillators have the potential to be resourced as reservoir computers due to their rich dynamics. Here, a Hopf oscillator is used as a physical reservoir computer by discarding the delay line and time-multiplexing procedure. A parametric study is used to uncover computational limits imposed by the dynamics of the oscillator using parity and chaotic time-series prediction benchmark tasks. Resonance, frequency ratios from the Farey sequence, and Arnold tongues were found to strongly affect the computation ability of the reservoir. These results provide insights into fabricating physical reservoir computers from limit cycle systems.

Articulating Resilience: Adaptive Locomotion of Wheeled Tensegrity Robot

Resilience plays an important role in improving robustness for robots in harsh environments such as planetary exploration and unstructured terrains. As a naturally compliant structure, tensegrity presents advantageous flexibility for enhancing resilience in robotic applications according to existing research. However, tensegrity robots to date are normally based on monolithic morphologies and are slow in locomotion. In this paper, we demonstrate how we adopt such flexibility to improve the robustness of wheeled robots by articulating modules with tensegrity mechanisms. The test results reveal the robot is resistant and resilient to external hazards in a fully passive manner owing to the continuous elasticity in the structure network. It possesses a good number of DoFs and can adapt to various terrains easily either with actuation or not. The robot is also capable of crawling locomotion aside from wheeled locomotion to traverse uneven surfaces and provide self-recovery from rollovers. It demonstrates good robustness and mobility at the same time compared with existing tensegrity robots and shows the competitiveness with conventional rigid robots in harsh scenarios. The proposed robot presents the capability of tensegrity robots with resilience, robustness, agility, and mobility without compromise. In a broader perspective, it widens the potential of tensegrity robots in practical applications.

Natural quantum reservoir computing for temporal information processing

Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.

Soft Robotic Perspective and Concept for Planetary Small Body Exploration

Tens of thousands of planetary small bodies (asteroids, comets, and small moons) are flying beside our Earth with little understanding. Explorers on the surfaces of these bodies, unlike the Lunar or Mars rovers, have only few attempts and no sophisticated solution. Current concerns mainly focus on landing uncertainties and mobility limitations, which soft robots may suitably aid utilizing their compliance and adaptivity. In this study, we present a perspective of designating soft robots for the surface exploration. Based on the lessons from recent space missions and an astronomy survey, we summarize the surface features of typical small bodies and the associated challenges for possible soft robotic design. Different kinds of soft mobile robots are reviewed, whose morphology and locomotion are analyzed for the microgravity, rugged environment. We also propose an alternative to current asteroid hoppers, as a case of applying progress in soft material. Specifically, the structure is a deployable cube whose outer shell is made of shape memory polymer, so that it can achieve morphing and variable stiffness between liftoff and landing phases. Dynamic simulations of the free-fall landing are carried out with a rigid counterpart for comparison. The results show that the soft deployed shell can effectively contribute to dissipating the kinetic energy and attenuating the excessive rebounds.

Development of a Modular Tensegrity Robot Arm Capable of Continuous Bending

In this study, we present a tensegrity robot arm that can reproduce the features of complex musculoskeletal structures, and can bend like a continuum manipulator. In particular, we propose a design method for an arm-type tensegrity robot that has a long shape in one direction, and can be deformed like a continuum manipulator. This method is based on the idea of utilizing simple and flexible strict tensegrity modules, and connecting them recursively so that they remain strict tensegrity even after being connected. The tensegrity obtained by this method strongly resists compressive forces in the longitudinal direction, but is flexible in the bending direction. Therefore, the changes in stiffness owing to internal forces, such as in musculoskeletal robots, appear more in the bending direction. First, this study describes this design method, then describes a developed pneumatically driven tensegrity robot arm with 20 actuators. Next, the range of motion and stiffness under various driving patterns are presented as evaluations of the robot performance.

Tensegrity Robotics

Numerous recent advances in robotics have been inspired by the biological principle of tensile integrity-or "tensegrity"-to achieve remarkable feats of dexterity and resilience. Tensegrity robots contain compliant networks of rigid struts and soft cables, allowing them to change their shape by adjusting their internal tension. Local rigidity along the struts provides support to carry electronics and scientific payloads, while global compliance enabled by the flexible interconnections of struts and cables allows a tensegrity to distribute impacts and prevent damage. Numerous techniques have been proposed for designing and simulating tensegrity robots, giving rise to a wide range of locomotion modes, including rolling, vibrating, hopping, and crawling. In this study, we review progress in the burgeoning field of tensegrity robotics, highlighting several emerging challenges, including automated design, state sensing, and kinodynamic motion planning.

A Hopf physical reservoir computer

Physical reservoir computing utilizes a physical system as a computational resource. This nontraditional computing technique can be computationally powerful, without the need of costly training. Here, a Hopf oscillator is implemented as a reservoir computer by using a node-based architecture; however, this implementation does not use delayed feedback lines. This reservoir computer is still powerful, but it is considerably simpler and cheaper to implement as a physical Hopf oscillator. A non-periodic stochastic masking procedure is applied for this reservoir computer following the time multiplexing method. Due to the presence of noise, the Euler-Maruyama method is used to simulate the resulting stochastic differential equations that represent this reservoir computer. An analog electrical circuit is built to implement this Hopf oscillator reservoir computer experimentally. The information processing capability was tested numerically and experimentally by performing logical tasks, emulation tasks, and time series prediction tasks. This reservoir computer has several attractive features, including a simple design that is easy to implement, noise robustness, and a high computational ability for many different benchmark tasks. Since limit cycle oscillators model many physical systems, this architecture could be relatively easily applied in many contexts.

Echo State Networks for Estimating Exteroceptive Conditions From Proprioceptive States in Quadruped Robots

We propose a methodology based on reservoir computing for mapping local proprioceptive information acquired at the level of the leg joints of a simulated quadruped robot into exteroceptive and global information, including both the ground reaction forces at the level of the different legs and information about the type of terrain traversed by the robot. Both dynamic estimation and terrain classification can be achieved concurrently with the same reservoir computing structure, which serves as a soft sensor device. Simulation results are presented together with preliminary experiments on a real quadruped robot. They demonstrate the suitability of the proposed approach for various terrains and sensory system fault conditions. The strategy, which belongs to the class of data-driven models, is independent of the robotic mechanical design and can easily be generalized to different robotic structures.

Tensegrity Metamaterials: Toward Failure-Resistant Engineering Systems through Delocalized Deformation

Failure of materials and structures is inherently linked to localized mechanisms, from shear banding in metals, to crack propagation in ceramics and collapse of space-trusses after buckling of individual struts. In lightweight structures, localized deformation causes catastrophic failure, limiting their application to small strain regimes. To ensure robustness under real-world nonlinear loading scenarios, overdesigned linear-elastic constructions are adopted. Here, the concept of delocalized deformation as a pathway to failure-resistant structures and materials is introduced. Space-tileable tensegrity metamaterials achieving delocalized deformation via the discontinuity of their compression members are presented. Unprecedented failure resistance is shown, with up to 25-fold enhancement in deformability and orders of magnitude increased energy absorption capability without failure over same-strength state-of-the-art lattice architectures. This study provides important groundwork for design of superior engineering systems, from reusable impact protection systems to adaptive load-bearing structures.

Skeletonizing the Dynamics of Soft Continuum Body from Video

Soft continuum bodies have demonstrated their effectiveness in generating flexible and adaptive functionalities by capitalizing on the rich deformability of soft material. Compared with a rigid-body robot, it is in general difficult to model and emulate the morphology dynamics of a soft continuum body. In addition, a soft continuum body potentially has an infinite degree of freedom, requiring considerable labor to manually annotate its dynamics from external sensory data such as video. In this study, we propose a novel noninvasive framework for automatically extracting the skeletal dynamics from video of a soft continuum body and show the applications and effectiveness of our framework. First, we demonstrate that our framework can extract skeletal dynamics from animal videos, which can be effectively utilized for the analysis of soft continuum body including animal motion. Next, we focus on a soft continuum arm, a commonly used platform in soft robotics, and evaluate the potential information-processing capability. Normally, to control such a high-dimensional system, it is necessary to introduce many sensors to completely capture the motion dynamics, causing the deterioration of the material's softness. We illustrate that the evaluation of the memory capacity and sensory reconstruction error enables us to verify the minimum number of sensors sufficient for fully grasping the state dynamics, which is highly useful in designing a sensor arrangement for a soft robot. Also, we release the software developed in this study as open source for biology and soft robotics communities, which contributes to automating the annotation process required for the motion analysis of soft continuum bodies.

Robustness evaluation for rolling gaits of a six-strut tensegrity robot

Locomotive robots based on tensegrities have recently drawn much attention from various communities. A common strategy to realize long-distance locomotion is combining several basic gaits that are designed in advance. Considering the unavoidable uncertainties of the environment and the real locomotive system, selecting the gaits with high robustness is essential to the implementation of long-distance locomotion of tensegrity robots. However, no quantitative approach for robustness evaluation of rolling gaits is reported in recent research work. In this study, a practical and quantitative method is proposed for the robustness evaluation of rolling gaits of tensegrity robots. A mathematical model is built to describe the evaluation process, and the success rate of rolling is adopted as an indicator of robustness. Sensitivity analysis and robust evaluation are conducted on the rolling gaits of a typical six-strut tensegrity robot. Specifically, the sensitivities of the rolling gaits to five uncertain parameters (i.e. tendon stiffness, initial tendon prestress, the equivalent mass of nodes, actuation lengths of actuators, and slope of ground) are investigated and discussed in detail, and the robustness of the rolling gaits is evaluated by correlated random sampling. Experiments on a physical prototype of the six-strut tensegrity robot are carried out to verify the proposed concept and method.

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.

3D-printed programmable tensegrity for soft robotics

Tensegrity structures provide both structural integrity and flexibility through the combination of stiff struts and a network of flexible tendons. These structures exhibit useful properties: high stiffness-to-mass ratio, controllability, reliability, structural flexibility, and large deployment. The integration of smart materials into tensegrity structures would provide additional functionality and may improve existing properties. However, manufacturing approaches that generate multimaterial parts with intricate three-dimensional (3D) shapes suitable for such tensegrities are rare. Furthermore, the structural complexity of tensegrity systems fabricated through conventional means is generally limited because these systems often require manual assembly. Here, we report a simple approach to fabricate tensegrity structures made of smart materials using 3D printing combined with sacrificial molding. Tensegrity structures consisting of monolithic tendon networks based on smart materials supported by struts could be realized without an additional post-assembly process using our approach. By printing tensegrity with coordinated soft and stiff elements, we could use design parameters (such as geometry, topology, density, coordination number, and complexity) to program system-level mechanics in a soft structure. Last, we demonstrated a tensegrity robot capable of walking in any direction and several tensegrity actuators by leveraging smart tendons with magnetic functionality and the programmed mechanics of tensegrity structures. The physical realization of complex tensegrity metamaterials with programmable mechanical components can pave the way toward more algorithmic designs of 3D soft machines.

Phase Changing Materials-Based Variable-Stiffness Tensegrity Structures

Soft robots leverage deformable bodies to achieve different types of locomotion, improve transportability, and safely navigate cluttered environments. In this context, variable-stiffness structures provide soft robots with additional properties, such as the ability to increase forces transmitted to the environment, to lock into different body configurations, and to reduce the number of actuators required for morphological change. Tensegrity structures have been recently proposed as a biologically inspired design principle for soft robots. However, the few examples of tensegrity structures with variable stiffness displayed relatively small stiffness change (i.e., by a factor of 3) or resorted to multiple and bulky actuators. In this article, we describe a novel design approach to variable-stiffness tensegrity structures (VSTSs) that relies on the use of variable-stiffness cables (VSCs). As an example, we describe the design and implementation of a three-strut tensegrity structure with VSCs made of low melting point alloys. The resulting VSTS displays unprecedented stiffness changes by a factor of 28 in compression and 13 in bending. We show the capabilities of the proposed VSTS in three validation scenarios with different tensegrity architectures: (1) a beam with tunable load-bearing capability, (2) a structure that can self-deploy and lock its shape in both deployed and undeployed states, and (3) a joint with underactuated shape deformations.

Rolling Locomotion of Cable-Driven Soft Spherical Tensegrity Robots

Soft spherical tensegrity robots are novel steerable mobile robotic platforms that are compliant, lightweight, and robust. The geometry of these robots is suitable for rolling locomotion, and they achieve this motion by properly deforming their structures using carefully chosen actuation strategies. The objective of this work is to consolidate and add to our research to date on methods for realizing rolling locomotion of spherical tensegrity robots. To predict the deformation of tensegrity structures when their member forces are varied, we introduce a modified version of the dynamic relaxation technique and apply it to our tensegrity robots. In addition, we present two techniques to find desirable deformations and actuation strategies that would result in robust rolling locomotion of the robots. The first one relies on the greedy search that can quickly find solutions, and the second one uses a multigeneration Monte Carlo method that can find suboptimal solutions with a higher quality. The methods are illustrated and validated both in simulation and with our hardware robots, which show that our methods are viable means of realizing robust and steerable rolling locomotion of spherical tensegrity robots.

Jumping Tensegrity Robot Based on Torsionally Prestrained SMA Springs

This paper introduces the addition of torsional prestrain into the manufacturing process of shape memory alloy (SMA) springs to form torsionally prestrained SMA springs. These springs have a better performance at the same power input for the same loads and same coil dimensions as regular SMA springs. A modified thermoconstitutive model was presented that can predict the behavior of the actuator based on the amount of torsional prestrain added into the manufacturing process, and a simple two-state model is used to predict its actuation stroke. These improved actuators were used in the development of a tensegrity robots capable of fast rolling motions and jumping both vertically and horizontally. This robot is capable of rolling at 0.14 BL/s (body length per second) and can jump up to nearly a full body length.

An Adaptive Bioinspired Foot Mechanism Based on Tensegrity Structures

Traditional robotic feet have received considerable attention for adaptive locomotion on complex terrain. As an alternative, tensegrity structures have the essential characteristics of deformability, adaptability to the environment, and impact resistance. This article proposes ways to solve the problem of adaptive locomotion on complex terrain based on a tensegrity structure and shows that this approach is particularly useful. On the basis of the locomotion mechanism and morphological structure of the human foot, a structural mapping model of a tetrahedral mast tensegrity structure is established through bionic mapping. A model of an adaptive foot mechanism is established through bioinspired design. Theoretical calculations of the behavior of the mechanism are derived, and the spring stiffnesses are matched. A theoretical method based on mechanical kinematics is presented, and a kinematic solution is realized through inverse kinematics. In addition, the locomotion of the mechanism, which is similar to that of the human foot, is simulated using ADAMS, and the effectiveness of the proposed theory and design method is verified by comparing the simulation output with the theoretically calculated results. Finally, a physical prototype manufactured using three-dimensional printing technology is used to experimentally verify the functional characteristics of the terrain-adaptive locomotion of the proposed mechanism. The results show that the proposed adaptive bioinspired foot mechanism exhibits good stability in an unstructured environment and can mimic the adaptive locomotion characteristics of the human foot on complex terrain remarkably well.

Adaptive tensegrity locomotion: Controlling a compliant icosahedron with symmetry-reduced reinforcement learning

Tensegrity robots, which are prototypical examples of hybrid soft–rigid robots, exhibit dynamical properties that provide ruggedness and adaptability. They also bring about, however, major challenges for locomotion control. Owing to high dimensionality and the complex evolution of contact states, data-driven approaches are appropriate for producing viable feedback policies for tensegrities. Guided policy search (GPS), a sample-efficient hybrid framework for optimization and reinforcement learning, has previously been applied to generate periodic, axis-constrained locomotion by an icosahedral tensegrity on flat ground. Varying environments and tasks, however, create a need for more adaptive and general locomotion control that actively utilizes an expanded space of robot states. This implies significantly higher needs in terms of sample data and setup effort. This work mitigates such requirements by proposing a new GPS -based reinforcement learning pipeline, which exploits the vehicle’s high degree of symmetry and appropriately learns contextual behaviors that are sustainable without periodicity. Newly achieved capabilities include axially unconstrained rolling, rough terrain traversal, and rough incline ascent. These tasks are evaluated for a small variety of key model parameters in simulation and tested on the NASA hardware prototype, SUPERball. Results confirm the utility of symmetry exploitation and the adaptability of the vehicle. They also shed light on numerous strengths and limitations of the GPS framework for policy design and transfer to real hybrid soft–rigid robots.

Kinodynamic planning for spherical tensegrity locomotion with effective gait primitives

Tensegrity-based robots can achieve locomotion through shape deformation and compliance. They are highly adaptable to their surroundings, and are lightweight, low cost, and physically robust. Their high dimensionality and strongly dynamic nature, however, can complicate motion planning. Efforts to date have primarily considered quasi-static reconfiguration and short-term dynamic motion of tensegrity robots, which do not fully exploit the underlying system dynamics in the long term. Longer-horizon planning has previously required costly search over the full space of valid control inputs. This work synthesizes new and existing approaches to produce dynamic long-term motion while balancing the computational demand. A numerical process based upon quasi-static assumptions is first applied to deform the system into an unstable configuration, causing forward motion. The dynamical characteristics of the result are then altered via a few simple parameters to produce a small but diverse set of useful behaviors. The proposed approach takes advantage of identified symmetries on the prototypical spherical tensegrity robot, which reduce the number of needed gaits but allow motion along different directions. These gaits are first combined with a standard search method to achieve long-term planning in environments where the developed gaits are effective. For more complex environments, the various motion primitives are paired with the fall-back option of random valid actions and are used by an informed sampling-based kinodynamic motion planner with anytime properties. Evaluations using a physics-based model for the prototypical robot demonstrate that modest but efficiently applied search effort can unlock the utility of dynamic tensegrity motion to produce high-quality solutions.

Recent advances in physical reservoir computing: A review

Reservoir computing is a computational framework suited for temporal/sequential data processing. It is derived from several recurrent neural network models, including echo state networks and liquid state machines. A reservoir computing system consists of a reservoir for mapping inputs into a high-dimensional space and a readout for pattern analysis from the high-dimensional states in the reservoir. The reservoir is fixed and only the readout is trained with a simple method such as linear regression and classification. Thus, the major advantage of reservoir computing compared to other recurrent neural networks is fast learning, resulting in low training cost. Another advantage is that the reservoir without adaptive updating is amenable to hardware implementation using a variety of physical systems, substrates, and devices. In fact, such physical reservoir computing has attracted increasing attention in diverse fields of research. The purpose of this review is to provide an overview of recent advances in physical reservoir computing by classifying them according to the type of the reservoir. We discuss the current issues and perspectives related to physical reservoir computing, in order to further expand its practical applications and develop next-generation machine learning systems.

Adaptive and Resilient Soft Tensegrity Robots

Living organisms intertwine soft (e.g., muscle) and hard (e.g., bones) materials, giving them an intrinsic flexibility and resiliency often lacking in conventional rigid robots. The emerging field of soft robotics seeks to harness these same properties to create resilient machines. The nature of soft materials, however, presents considerable challenges to aspects of design, construction, and control-and up until now, the vast majority of gaits for soft robots have been hand-designed through empirical trial-and-error. This article describes an easy-to-assemble tensegrity-based soft robot capable of highly dynamic locomotive gaits and demonstrating structural and behavioral resilience in the face of physical damage. Enabling this is the use of a machine learning algorithm able to discover effective gaits with a minimal number of physical trials. These results lend further credence to soft-robotic approaches that seek to harness the interaction of complex material dynamics to generate a wealth of dynamical behaviors.