• Proposes a Digital Twin framework for EVs routing and charging optimization • Develops a Dual-Population Evolutionary Algorithm regarding to the dynamic environments • Demonstrates robustness under traffic disruptions, road closures, and charging station failures • Enhances adaptability and efficiency of EV operations in urban traffic networks The growing adoption of electric vehicles (EVs) presents new challenges for intelligent transportation systems (ITS), particularly in dynamic traffic environments where routing and charging decisions must adapt to fluctuating conditions. This paper proposes a Digital Twin-based Electric Vehicle Routing and Charging approach (DT-EVRC) that integrates real-time traffic data, predictive analytics, and a Dual-Population Evolutionary Algorithm (DPEA) to optimize EV travel and charging schedules. Unlike traditional static or simplified models, DT-EVRC continuously synchronizes with the physical transportation network, capturing variations in traffic density, charging station availability, and energy constraints. Experimental results on diverse grid-based urban scenarios demonstrate that DT-EVRC achieves robust and adaptive performance under traffic disruptions, road closures, and charging station failures. The proposed approach highlights the potential of digital twin technologies, combined with advanced optimization, to support next-generation ITS by enabling efficient, resilient, and sustainable urban mobility.

Swarm intelligence results from a collective behaviour of swarm network, which harnesses distributed and simple rules of swarm systems to address complex problems without a central controller. One potential approach to transform such swarm networks into valuable and practical computational resources is by applying the reservoir computing framework. However, technical challenges, such as permutation symmetry and instability, could emerge in these networks during the process, which significantly hinder the computational performance. In this paper, we explore the potential of mobile swarm networks in a reservoir computing framework to perform machine learning tasks. We propose an observation layer using Gaussian kernel density estimation to be inserted into the reservoir computing framework. Our approach not only addresses permutation symmetry but also stabilises swarm behaviours, resulting in a scalable swarm network. We explore variations in computational capacity across different swarm sizes and combinations with four benchmark computations. We prove the effectiveness of our observation layer in addressing permutation symmetry and discovered the improvement in performance in combining different swarm networks in parallel. We found that the best ratio between ants and birds reservoir is 8:2. The performance achieves a covariance of approximately 0.20 with a swarm size of 20, comparable to that of echo-state-network (ESN) with 16 nodes. As the swarm size increases to 60, the covariance value reaches around 0.21, matching the performance of ESN with 18 nodes. This indicates that our swarm network has a reasonable amount of memory and nonlinearly capacity in performing computation tasks. We also validate our method's effectiveness on a handwriting classification task, further highlighting its practical applicability. Our findings delve into the impacts of the swarm networks' computational abilities, offering insights into mechanisms in this alternative means of swarm intelligence and application to AI.

Accurate robotic liquid manipulation has been a challenging task for many industry sectors, which requires the robot to sufficiently understand the liquid flow behaviour. Physics simulation-informed liquid perception and visual or tactile-based direct liquid sensing have been explored as useful approaches for liquid manipulation. However, these approaches may not be practical to implement in some resource and space-limited cases as they require a physical robot to learn the liquid flow behaviour through physical interaction with the liquid. This paper proposes a liquid perception framework where a low-cost and robot-free flow cup test (a standard fluidity characterisation test) is utilised to capture the physical liquid flow behaviour which is then transformed into a position-based physics simulation during a virtual flow cup test using Bayesian optimisation. Such liquid perception through the ’real-to-sim’ flow cup test is intended for guiding robot operations in any subsequent liquid manipulation tasks. The viability of the proposed framework is examined by comparing the physical liquid manipulation performance (i.e., accuracy) with that in simulation using the learned liquid flow behaviour. A robotic crack sealing experiment is implemented as a validation use case in manufacturing. The results suggest that the proposed framework is able to capture and predict a random liquid flow behaviour at a statistical mean accuracy of 85.4-87.0% (with a high-probability accuracy of 88-90%) in approx. 6 minutes using the computation resource in this study, validating its feasibility for underpinning general robotic liquid manipulation applications.

Adapting reinforcement learning (RL) policies to various robot body configurations is a significant challenge for creating flexible autonomous systems. This study presents a novel framework that integrates Reservoir Computing (RC) with the First-Order Reduced and Controlled Error (FORCE) learning rule to enhance policy adaptability in RL. The RC serves as a dynamic feature extractor, capturing temporal dependencies by pre-training on state transitions generated through random actions. This pre-training acts as regularization, reducing variance and preventing overfitting to specific configurations Subsequently, the control policy network is trained on a limited set of body variations using the enriched features from the RC. Experimental results across three distinct environments demonstrate that the proposed RC+FORCE framework significantly improves policy performance and adaptability to unseen robot configurations compared to traditional reinforcement learning through domain randomization. These findings highlight the effectiveness of combining RC-based feature extraction with FORCE-based training in developing robust RL agents.

Fast-legged humanoid robots are transforming industries from manufacturing to medical robotics, with the global market projected to grow from $0.67 billion in 2024 to $2.27 billion by 2033 at a 14.3% CAGR. Despite rapid advancements, challenges remain in navigating complex terrains, especially uneven, deformable, and high-friction surfaces. This paper presents the first minimally sensorised blade leg made by coupling soft and rigid materials for robots: an alternative approach for multimodal sensing and advanced control algorithms in terrain navigation. This incorporates a passive leg design embedded with barometric pressure sensors that are proven to retain high dimentional spatio-temporal data. Hence we hypothesized that barometric pressure sensors can capture multidimensional terrain data and subtle surface compliance changes through spatiotemporal pressure patterns. The blade was mounted on an UR5 robotic arm and tested in terrains of varied textures, including aluminium, pebble, coir, and sandpaper; materials spanning a diverse range of stiffness. Spatiotemporal data from the sensors were recorded and analyzed to assess terrain characteristics and leg-terrain interactions under different conditions. The results demonstrated that barometric pressure sensors could accurately recognize different terrains with as few as three sensors in a 2-second time frame. Recognition accuracy improved with more sensors, demonstrating the effectiveness of morphologically adapted composite structures with optimally placed minimal sensors.

Soil is a vital resource for various industries, including agriculture, engineering, and manufacturing, where accurate in-situ classification is essential for a wide range of applications. Electrical Impedance Tomography (EIT) enables real-time soil classification by capturing complex impedance data across varying distances. This study presents a novel approach integrating EIT with actuating probes to dynamically generate rich datasets for distinguishing soil types and moisture levels. By utilizing eight moving electrodes multiplexed across 32 channels, this system overcomes the limitations of traditional laboratory-based methods, such as time constraints and data skew caused by non-homogeneous inclusions. The moving electrode design significantly outperforms the stationary setup by 21%, achieving an average classification accuracy of 93% across varying moisture levels of sand, clay, and silt combinations. Experimental results on a larger data set demonstrates a classification accuracy of up to 79.7% across 25 different soil-moisture combinations, underscoring the technique’s potential for effective in-field soil analysis The improved accuracy achieved through actuation, compared to stationary probes, suggests broader applications in precision agriculture, civil engineering, and environmental monitoring.

Torque-restricted control remains a significant challenge in robotics, often necessitating precise modeling or large amounts of data for effective controller design. To address this problem, we introduce a novel training method that utilizes a Reservoir Computing (RC) framework to serve as a model-free controller that can effectively control a nonlinear robot using minimal training. This paper explores the application of the proposed framework to a torque-restricted single pendulum and achieves similar control performance to that of model-free reinforcement learning controllers while utilising just 0.5% of the data and a simple passive data collection method. We analyze 1,000 unique successful reservoir structures, examining their internal connectivity and memory properties, and identify key structural features that enhance control performance. Finally, this paper also explores our proposed controller’s robustness to changes in pendulum dimensionality and torque limit with successful control achieved for a large range of varying properties without any additional training.

Advances in robotics have been driving the development of human-robot interaction (HRI) technologies. However, accurately perceiving human actions and achieving adaptive control remains a challenge in facilitating seamless coordination between human and robotic movements. In this paper, we propose a hierarchical procedural framework to enable dynamic robot-assisted hand-object interaction (HOI). An open-loop hierarchy leverages the RGB-based 3D reconstruction of the human hand, based on which motion primitives have been designed to translate hand motions into robotic actions. The low-level coordination hierarchy fine-tunes the robot’s action by using the continuously updated 3D hand models. Experimental validation demonstrates the effectiveness of the hierarchical control architecture. The adaptive coordination between human and robot behavior has achieved a delay of ≤ 0.3 seconds in the tele-interaction scenario. A case study of ring-wearing tasks indicates the potential application of this work in assistive technologies such as healthcare and manufacturing.

Autonomous vehicles (AVs) are vehicles that traverse on the road without active human intervention. With a coordinator, AVs can be connected to provide high-efficiency transport services, such as AV-based public transport networks. The controller can manage the network by coordinating the transport request assignment, traveling, and charging/discharging schedule. On the other hand, AVs are likely to be electric and benefit the smart grid via vehicle-to-grid technology. A well-designed mobility network connecting electric AVs (EAVs) and smart grid can substantially reduce unnecessary travel and energy costs. In this article, we aim to maximize utilities in the AV-based public transport network and the power distribution network for the vehicle network containing EAVs, charging stations, and distributed power generations. We formulate the assignment and scheduling problem as a multiobjective mixed-integer program (MIP). To solve the optimization problem, we develop a hybrid heuristic approach based on nondominated sorting genetic algorithm II (NSGA-II) and branch-and-bound (BnB) algorithms. Experiments are conducted on a modified 15-bus distribution system and a simulated traffic network. The results show that the proposed strategy effectively minimizes the total travel and energy purchase cost by 21%. This study provides valuable insights on vehicle coordination for multiple tasks, offering visionary guidance for stakeholders engaged in multifaceted transportation endeavors.

Teleoperated medical robots have the potential to revolutionize healthcare. However, when developing systems for tasks like remote palpation, state-of-the-art literature still uses test phantoms of oversimplified geometries, due to the complexity of the required mechanical robot–patient interaction. In reality, human bodies have complex 3-D shapes and require fine-tuning of all six manipulator's degrees of freedom, controlled by the user. In this article, we argue that the implementation of depth-vision-driven autonomous dimensionality-reduction (DVD ADR) shared control can greatly improve the users' performance. The proposed control method keeps the user in control of the end-effector’s position, while automatically adjusting its orientation in order to maintain the tactile sensor normal to the phantom's surface. A depth camera and a computer vision algorithm are used to infer the phantom's shape and achieve DVD ADR shared control. Experimental results showcase how this leads to statistically significant performance improvement. Not only were the participants able to achieve more precise palpations, with up to 29.5% and 22.4% more accuracy in position and orientation, respectively, but the DVD ADR shared control allowed them to achieve a 8.8% better detection accuracy while needing 13.8% less time. The abovementioned results are all tested for statistical significance and achieved ap-value lower than 0.05.

Soft robotics, an emerging domain in modern robotics, introduces innovative possibilities alongside challenges in controllability, particularly with multi-degree inflatable actuators. We present a novel manipulation method using underactuated soft fingers that addresses these challenges by harnessing symmetry breaking. Central to our approach is the mechanism of self-organization within a ring actuator equipped with five fingers. Typically considered a drawback, we exploit the actuator’s buckling behavior to facilitate in-hand manipulation. This strategic utilization enables object motion in both clockwise and counterclockwise directions via system perturbations and adjustments in frequency and duty cycle parameters. Employing the self-organizing properties of our actuator, our method is empirically validated through simulations and real actuator experiments, demonstrating the system’s ability in manipulating objects by leveraging the inherent flexibility and morphological advantages. The design enables two degrees of freedom with minimal input, allowing objects to rotate due to the actuator’s self-organizing actions. This simplification of control mechanisms is essential for soft robotics manipulation. Our findings indicate that control systems in soft robotics can be significantly simplified, harnessing the adaptable behavior inherent in its morphology.

Soft robotics has emerged as the standard solution for grasping deformable objects, and has proven invaluable for mobile robotic exploration in extreme environments. However, despite this growth, there are no widely adopted computational design tools that produce quality, manufacturable designs. To advance beyond the diminishing returns of heuristic bio-inspiration, the field needs efficient tools to explore the complex, non-linear design spaces present in soft robotics, and find novel high-performing designs. In this work, we investigate a hierarchical design optimization methodology which combines the strengths of topology optimization and quality diversity optimization to generate diverse and high-performance soft robots by evolving the design domain. The method embeds variably sized void regions within the design domain and evolves their size and position, to facilitating a richer exploration of the design space and find a diverse set of high-performing soft robots. We demonstrate its efficacy on both benchmark topology optimization problems and soft robotic design problems, and show the method enhances grasp performance when applied to soft grippers. Our method provides a new framework to design parts in complex design domains, both soft and rigid.

Virtual Model Control (VMC) is an approach to design a controller for force-controlled robots in complex uncertain environments. While this method was primarily investigated for legged robot locomotion in the past, it can be more generally applicable to other types of robotic systems. This paper investigates the VMC framework for reaching tasks in a force-controlled robotic arm. We propose six different approaches to designing virtual models in order to achieve reaching tasks in environments with obstacles and uncertainties. A force-controlled 8 degree-of-freedom humanoid robot was used to validate the proposed approach in the real world. We conducted three experiments to test the performance of VMC controllers in terms of predictability, sensitivity to external force, and adaptability against known and unknown obstacles. Experimental analyses show that, even though the proposed approach needs to sacrifice accuracy and trajectory optimality, it enables us to design complex reaching motions under uncertainties, in an intuitive and extendable manner.

Switching dynamics are prevalent in real-world systems, arising from either intrinsic changes or responses to external influences, which can be appropriately modeled by switched systems. Control synthesis for switched systems, especially integrating safety constraints, is recognized as a significant and challenging topic. This study focuses on devising a learning-based control strategy for switched nonlinear systems operating under arbitrary switching law. It aims to maintain stability and uphold safety constraints despite limited system data. To achieve these goals, we employ the control barrier function method and Lyapunov theory to synthesize a controller that delivers both safety and stability performance. To overcome the difficulties associated with constructing the specific control barrier and Lyapunov function and take advantage of switching characteristics, we create a neural control barrier function and a neural Lyapunov function separately for control policies through a state transition learning approach. These neural barrier and Lyapunov functions facilitate the design of the safe controller. The corresponding control policy is governed by learning from two components: policy loss and forward state estimation. The effectiveness of the developing scheme is verified through simulation examples.

Recent advances in machine learning have paved the way for the development of musical and entertainment robots. However, human–robot cooperative instrument playing remains a challenge, particularly due to the intricate motor coordination and temporal synchronization. In this article, we propose a theoretical framework for human–robot cooperative piano playing based on nonverbal cues. First, we present a music improvisation model that employs a recurrent neural network (RNN) to predict appropriate chord progressions based on the human's melodic input. Second, we propose a behavior-adaptive controller to facilitate seamless temporal synchronization, allowing the cobot to generate harmonious acoustics. The collaboration takes into account the bidirectional information flow between the human and robot. We have developed an entropy-based system to assess the quality of cooperation by analyzing the impact of different communication modalities during human–robot collaboration. Experiments demonstrate that our RNN-based improvisation can achieve a 93% accuracy rate. Meanwhile, with the MPC adaptive controller, the robot could respond to the human teammate in homophony performances with real-time accompaniment. Our designed framework has been validated to be effective in allowing humans and robots to work collaboratively in the artistic piano-playing task.

As soft robotic systems become increasingly complex, there is a need to develop sensory systems which can provide rich state information to the robot for feedback control. Multi-axis force sensing and control is one of the less explored problems in this domain. There are numerous challenges in the development of a multi-axis soft sensor: from the design and fabrication to the data processing and modelling. This work presents the design and development of a novel multi-axis soft sensor using a gelatin-based ionic hydrogel and 3D printing technology. A learning-based modelling approach coupled with sensor redundancy is developed to model the environmentally dependent soft sensors. Numerous real-time experiments are conducted to test the performance of the sensor and its applicability in closed-loop control tasks at 20 Hz. Our results indicate that the soft sensor can predict force values and orientation angle within 4% and 7% of their total range, respectively.

The ability to express diverse behaviors is a key requirement for most biological systems. Underpinning behavioral diversity in the natural world is the embodied interaction between the brain, body, and environment. Dynamical systems form the basis of embodied agents, and can express complex behavioral modalities without any conventional computation. While significant study has focused on designing dynamical systems agents with complex behaviors, for example, passive walking, there is still a limited understanding about how to drive diversity in the behavior of such systems. In this article, we present a novel hardware platform for studying the emergence of individual and collective behavioral diversity in a dynamical system. The platform is based on the so-called Bernoulli ball, an elegant fluid dynamics phenomenon in which spherical objects self-stabilize and hover in an airflow. We demonstrate how behavioral diversity can be induced in the case of a single hovering ball via modulation of the environment. We then show how more diverse behaviors are triggered by having multiple hovering balls in the same airflow. We discuss this in the context of embodied intelligence and open-ended evolution, suggesting that the system exhibits a rudimentary form of evolutionary dynamics in which balls compete for favorable regions of the environment and exhibit intrinsic "alive" and "dead" states based on their positions in or outside of the airflow.

Soft continuum robots are highly flexible and adaptable, making them ideal for unstructured environments such as the human body and agriculture. However, their high compliance and maneuverability make them difficult to model, sense, and control. Current control strategies focus on Cartesian space control of the end-effector, but few works have explored full-body control. This study presents a novel image-based deep learning approach for closed-loop kinematic shape control of soft continuum robots. The method combines a local inverse kinematics formulation in the image space with deep convolutional neural networks for accurate shape control that is robust to feedback noise and mechanical changes in the continuum arm. The shape controller is fast and straightforward to implement; it takes only a few hours to generate training data, train the network, and deploy, requiring only a web camera for feedback. This method offers an intuitive and user-friendly way to control the robot's 3-D shape and configuration through teleoperation using only 2-D hand-drawn images of the desired target state without the need for further user instruction or consideration of the robot's kinematics.

Soft robotic grippers are becoming increasingly popular for agricultural and logistics automation. Their passive conformability enables them to adapt to varying product shapes and sizes, providing stable large-area grasps. This work presents a novel methodology for combining soft robotic grippers with electrical impedance tomography-based sensors to infer intrinsic properties of grasped fruits. We use a Fin Ray soft robotic finger with embedded microspines to grab and obtain rich multi-direction electrical properties of the object. Learning-based techniques are then used to infer the desired fruit properties. The framework is extensively tested and validated on multiple fruit groups. Our results show that ripeness parameters and even weight of the grasped fruit can be estimated with reasonable accuracy autonomously using the proposed system.

Soft sensorised skins are essential for improving robotic manipulation capabilities towards that of humans. Integration of sensors into existing robotic hands is challenging due to rigidity of components, low packing density or poor sensor response. We propose a sensorised skin, based-on barometric sensing, which can be molded over a skeletal robot hand. The sensors connect air chambers embedded in the soft skin to wrist-mounted pressure sensors, allowing sensor spacing 2–4 mm, force ranges from 23 mN to 5700 mN and bandwidth of 20 Hz. Integrating this with a skeletal hand allows us to showcase the potential of these sensors to aid robotic manipulation. We demonstrate 3-axis contact modelling, useful for in-hand manipulation and exploration. In addition, by grasping a chopstick and sensing forces transmitted from the environment, the system can remotely detect small environmental features, e.g., hole finding using tools.

Soft grippers have the potential to solve many existing manipulation challenges, particularly in agile industry applications. However, existing soft grippers are often limited in the range of objects they can pick, or by cluttered environments. We present a design inspired by the nose and fingers at the end of an elephant's trunk, which can pick both by suction and pinching, allowing increased grasping diversity. In addition, we observe an emergent grasping mode, a hybrid of pinching and suction where the cup aperture is morphed online, using embedded soft fingers, to form a seal over challenging objects. An algorithmic grasping strategy, based-on analytical grasping models and primitive objects, is presented. With this, we predict grasping performance and show increased grasping range compared to other soft gripper designs. Finally, the gripper and grasping strategy are successfully applied to grasping more varied everyday objects, demonstrating exploitation of this multi-modal gripping for adaptive grasping.

Behavioral diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context we are interested in falling paper systems, specifically the V-shaped falling paper (VSFP) system that exhibits a set of discrete falling behaviors across the morphological parameter space. Our previous work has investigated how morphology influences dominant falling behaviors in the VSFP system. In this article we build on this analysis to investigate the nature of behavioral transitions in the same system. First, we investigate stochastic behavior transitions. We demonstrate how morphology influences the likelihood of different transitions, with certain morphologies leading to a wide range of possible paths through the behavior-space. Second, we investigate deterministic transitions. To investigate behaviors over longer time periods than available in falling experiments we introduce a new experimental platform. We demonstrate how we can induce behavior transitions by modulating the energy input to the system. Certain behavior transitions are found to be irreversible, exhibiting a form of hysteresis, while others are fully reversible. Certain morphologies are shown to behave like simplistic sequential logic circuits, indicating that the system has a form of memory encoded into the morphology-environment interactions. Investigating the limits of how morphology-environment interactions induce non-trivial behaviors is a key step for the design of embodied artificial life-forms.

We had the great pleasure of organising the first virtual workshop on Embodied Intelligence, held on March 24–26, 2021. After the long struggle of more than a year with the pandemic, all of us were in strong need of interdisciplinary cross-fertilization events, even in a severely limited virtual setting. Even though it was a difficult time to organise anything, we had the luck of attracting over 1,000 registered participants to this event, with more than 100 presentations along with many active debates and discussions. Some of these lectures and debates are available at https://embodied-intelligence.org/.Because of the very successful event, we decided to organise this Special Issue on Embodied Intelligence in the Artificial Life journal to capture some of the discussions and document them in the format of journal publications. For this reason, the authors and reviewers of this special issue were mostly participants of the workshop. We are excited to deliver this issue to reflect the progress and challenges in this research field. The articles included in this special issue are as follows.“Machines that Feel and Think: The Role of Affective Feelings and Mental Action in (Artificial) General Intelligence” by George Deane discusses the roles of feelings, emotions, and moods for understanding biological intelligence and achieving artificial general intelligence. With ongoing research on active inference and self-modelling, the article argues that research in “affective feelings” plays increasingly essential roles to obtain a better understanding of computational phenomenology.“The Enactive and Interactive Dimensions of AI: Ingenuity and Imagination Through the Lens of Art and Music” by Maki Sato and Jonathan McKinney discusses the contributions of embodied and enactive approaches to AI, with a detailed analysis of an aspect of Japanese philosophy in terms of interactivity and contingent dimensions.“Evolving Modularity in Soft Robots Through an Embodied and Self-Organizing Neural Controller” by Federico Pigozzi and Eric Medvet presents research achievements in evolved soft robots. The roles of morphologies and the distributed nature of control architecture were analyzed with respect to the evolution of modularity in various simulated agents.“Braitenberg Vehicles as Developmental Neurosimulation” by Stefan Dvoretskii et al. presents recent progress in research in the developmental approach applied to the neural network of Braitenberg vehicles. Implementation of the basic principles from developmental sciences was shown to lead to the emergence of simple cognitive processes such as feedback, spatial perception, and collective behaviours.“An Embodied Intelligence-Based Biologically Inspired Strategy for Searching a Moving Target” by Julian K. P. Tan et al. reported recent analysis on search behaviours of simulated agents inspired by E. coli. The effect of embodiment was investigated to explain how simple biological systems can take advantage of it for survival.The vast field of embodied intelligence research cannot be easily covered in a single special issue, but these articles nicely bring the spirit of this research field to the Artificial Life journal. In particular, the interdisciplinary nature of Embodied Intelligence research, from basic technical research on soft robotics and mobile robots to cognitive science and philosophy, was the real fertile basis of innovative fundamental research. We hope that readers enjoy the excitement of the progress reported in this field and join the discussions in future activities of the embodied intelligence researcher community.

A bio-inspired way to model locomotion is using a network of coupled phase oscillators to create a Central Pattern Generator (CPG). The recently developed feedback control method tegotae includes exteroceptive force feedback into the governing phase update equations, leading to gait limit cycles. However, the oscillator coupling weights are often determined empirically. Here, we first investigate how the coupling coefficients influence the limit cycle convergence behavior on a 2- and 3-limbed structure in simulation. We find that the convergence with tegotae can be improved by introducing appropriate cross-couplings. This results in a smoother convergence and steady-state behavior where each individual oscillator drives the full network to a common convergence state in comparison to competing convergence states with ill-chosen cross-couplings. We then validate the findings in hardware and hypothesize how the appropriate couplings could be derived directly from the morphology, potentially eliminating the empiric determination.

The sense of taste is fundamental to a human chef’s ability to cook tasty food. To develop robots that can demonstrate human-like cooking, robots need to be equipped with a sense of taste and enabled to use this perception capability to improve or understand the food which they are cooking. We propose a first study of using a salinity sensor to provide a robot with a sense of saltiness. We then demonstrate how this artificial taste receptor can be used to create an autonomous closed-loop cooking system that uses a measurement of saltiness to improve the cooking process of preparing scrambled eggs. Specifically, we show that the sensor measurements can be mapped to different taste metrics such as the overall saltiness and state of mixing the eggs, and how the cooking process can be adapted to match a human-cooked dish, hence individual preferences.

Robotic phantoms enable advanced physical examination training before using human patients. In this article, we present an abdominal phantom for palpation training with controllable stiffness liver nodules that can also sense palpation forces. The coupled sensing and actuation approach is achieved by pneumatic control of positive-granular jammed nodules for tunable stiffness. Soft sensing is done using the variation of internal pressure of the nodules under external forces. This article makes original contributions to extend the linear region of the neo-Hookean characteristic of the mechanical behavior of the nodules by 140% compared to no-jamming conditions and to propose a method using the organ level controllable nodules as sensors to estimate palpation position and force with a root-mean-square error of 4% and 6.5%, respectively. Compared to conventional soft sensors, the method allows the phantom to sense with no interference to the simulated physiological conditions when providing quantified feedback to trainees, and to enable training following current bare-hand examination protocols without the need to wear data gloves to collect data.

Mechanical self-stability is often useful for controlling systems in uncertain and unstructured environments because it can regulate processes without explicit state observation or feedback computation. However, the performance of such systems is often not optimised, which begs the question how their dynamics can be naturally augmented by a control law to improve performance metrics. We propose a minimalistic approach to controlling mechanically self-stabilising systems by utilising model-based, feedforward bang-bang control at a global level and self-stabilizing dynamics at a local level. We demonstrate the approach in the height control problem of a sphere hovering in a vertical air jet - the so-called Bernoulli Ball. After developing a model to study the system and theoretically proving global asymptotic stability, we present the augmented controller and show how to enhance performance measures and plan behaviour. Our physical experiments show that the proposed control approach has a reduced time-to-target compared to the uncontrolled system without loss of stability (ranging from a 2.4 to 4.4 fold improvement) and that we can plan sequences of target positions at will.

Abstract 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.

To match the ever increasing standards of fresh products, and the need to reduce waste, we devise an alternative to the destructive and highly variable fruit ripeness estimation by a penetrometer. We propose a fully automatic method to assess the ripeness of mango which is non-destructive, allows the user to test multiple surface areas with a single touch and is capable of dissociating between ripe and non-ripe fruits. A custom-made gripper equipped with a capacitive tactile sensor array is used to palpate the fruit. The ripeness is estimated as mango stiffness extracted through a simplified spring model. We test the framework on a set of 25 mangoes of the Keitt variety, and compare the results to penetrometer measurements. We show it is possible to correctly classify 88% of the mango without removing the skin of the fruit. The method can be a valuable substitute for non-destructive fruit ripeness testing. To the authors knowledge, this is the first robotics ripeness estimation system based on capacitive tactile sensing technology.

Morphological computation is a concept relevant to robots made of soft and elastic materials. It states that robot's rich dynamics can be exploited to generate desirable behaviors, which can be altered when their morphology is adapted accordingly. This paper presents a low-cost robot made of elastic curved beam driven by a motor, with morphological computation and adaptation ability. Simply by changing robot's shape and the rotating frequency of the motor that vibrates the robot's body, the robot is able to shift its behavior from showing a tendency to slide when it needs to perform tasks like going under confined space, to have more tendency to hop diagonally forward when the robot stands upright. It will also be shown that based on the proposed mechanism, the energy efficiency of the robot locomotion can be maximized.

Prefabrication of structures is currently used in a limited capacity, due to the lack of flexibility, despite the potential cost and speed advantages. Autonomous flexible reassembly enables structures to be developed which can be continuously and iteratively dis-assembled and re-assembled providing far more flexibility in comparison to single shot pre-fabrication methods. Dis-assembly of structures should be considered when assembling, due to the asymmetry of assembly and dis-assembly processes, to ensure structures can be recycled and re-assembled. This allows for agile development, significantly reducing the time and resource usage during the build process. In this work, a framework for flexible re-assembly is developed and a robotic platform is developed to implement and test this framework with simple Lego bricks. The tradeoffs in terms of time, resource use and probability of success of this new assembly method can be understood by using a cost function to compare to alternative fabrication methods.

Impulsive actuation enables robots to perform agile maneuvers and surpass difficult terrain, yet its capacity to induce continuous and stable locomotion have not been explored. We claim that strictly convex foot shapes can improve the impulse effectiveness (impulse used per travelled distance) and locomotion speed by facilitating periodicity and stability. To test this premise, we introduce a theoretical 2-D model based on rigid-body mechanics to prove stability. We then implement a more elaborate model in simulation to study transient behavior and impulse effectiveness. Finally, we test our findings on a robot platform to prove their physical validity. Our results prove that continuous and stable locomotion can be achieved in the strictly convex case of a disk with an off-centered mass. In keeping with our theory, stable limit cycles of the off-centered disk outperform the theoretical performance of a cube in simulation and experiment, using up to 10 times less impulse per distance to travel at the same locomotion speed.

There is an increasing interest in the use of soft technologies for robotic application, however, the lack of advanced sensory motor capabilities is currently a significant limitation. Sensing of soft robots, in particular, is still not fully understood and methods by which a large deformable continuum body can be effectively sensed without loosing the intrinsic soft body dynamics are limited. This paper proposes a novel soft body sensing method, localized differential sensing, whereby the sensing of localized deformation on a large soft structure can be achieved with a pair of strain sensors. The design principles for this method are given. To demonstrate this sensing method Conductive Thermoplastic Elastomer (CTPE) is used for the sensing of deformation of soft body structures. A feasibility study of the approach is also presented, with strain sensors incorporated into the universal gripper to allow detection of grasped objects.

Evolutionary algorithms have previously been applied to the design of morphology and control of robots. The design space for such tasks can be very complex, which can prevent evolution from efficiently discovering fit solutions. In this article we introduce an evolutionary-developmental (evo-devo) experiment with real-world robots. It allows robots to grow their leg size to simulate ontogenetic morphological changes, and this is the first time that such an experiment has been performed in the physical world. To test diverse robot morphologies, robot legs of variable shapes were generated during the evolutionary process and autonomously built using additive fabrication. We present two cases with evo-devo experiments and one with evolution, and we hypothesize that the addition of a developmental stage can be used within robotics to improve performance. Moreover, our results show that a nonlinear system-environment interaction exists, which explains the nontrivial locomotion patterns observed. In the future, robots will be present in our daily lives, and this work introduces for the first time physical robots that evolve and grow while interacting with the environment.

Despite intensive investigations in the past, energetic efficiency is still one of the most important unsolved challenges in legged robot locomotion. This paper presents an unconventional approach to the problem of energetically efficient legged locomotion by applying actuation for spring mass running. This approach makes use of mechanical springs incorporated in parallel with relatively low-torque actuation, which is capable of both accommodating large payload and locomotion with low power input by exploiting self-excited vibration. For a systematic analysis, this paper employs both simulation models and physical platforms. The experiments show that the proposed approach is scalable across different payload between 0 and 150 kg, and is able to achieve a total cost of transport of 0.10, which is significantly lower than the previous locomotion robots and most of the biological systems in the similar scale, when actuated with the near-to natural frequency with the maximum payload.

For legged devices, their ability of carrying payload is a necessity for a wide range of tasks. In this paper, we present a new approach of carrying payload by using a parallel elastic mechanism, which is able to carry payloads of at least 3 times of its bodyweight. Although the robot has no sensory feedback and consists of only two rigid bodies and one spring loaded joint, it is able to achieve efficient and stable forward hopping for a wide range of attached payload. The presented payload carrier ETH Cargo is based on the further development of our platform CHIARO for the payload range between 0 and 100kg. After parameter optimization using simulations, a series of real world experiments prove stable and high efficiency hopping of the prototype over a wide range of payloads.

The realization and utilization of multimodal locomotion to enable robots to accomplish useful tasks is a significantly challenging problem in robotics. Related to the challenge, it is crucial to notice that the locomotion dynamics of the robots is a result of interactions between a particular control structure and its body-environment dynamics. From this perspective, this paper presents a simple control structure known as Attractor Selection Mechanism that enables a robot to self organize its multiple locomotion modes for accomplishing a goal-directed locomotion task. Despite the simplicity, the approach enables the robot to automatically explore different body-environment dynamics and stabilize onto particular attractors which corresponds to locomotion modes relevant to accomplish the task. The robot used throughout the paper is a curved-beam hopping robot, which despite its simple actuation method, possesses rich and complex body-environment dynamics.

Several approaches have been presented which allow robots to build structures to adapt themselves or their environments. To autonomously build these structures, a design must be made, from which instructions for the fabrication process can be derived. For a constrained fabrication process, e.g. considering the limited range of a robot, this transfer can be cumbersome. We present a local building process based on a sequence of two distinct operations, which implicitly encodes the shape of a structure. Given this encoding, the structure can readily be built with a real-world robotic system. We show automatic design of structures reaching out of the robot's range and fulfilling stability and strength constraints using an evolutionary design algorithm. The final design can then be built with a robotic arm from wooden cubes and hot melt adhesives. We demonstrate the whole process including the construction of a structure from more than thirty cubes with our real-world setup. We expect that automatic design and construction can further improve the physical adaptability of robotic systems.

Reduction of the system complexity is currently one of the main challenges for efficient and versatile legged robot locomotion. In this paper, we present a new one legged hopping robot called CHIARO, which is equipped with a curved foot. Even though the robot has no sensory feedback and consists of only two rigid bodies and one spring loaded joint with parallel actuation, it is able to achieve stable forward-hopping over a wide range of parameters and forward-speeds. Operating at natural hopping frequency, the parallel actuation shows good efficiency. This paper presents an approach to determine stability and efficiency of a highly non-linear mechanical system. By implementing a two dimensional numerical model, taking into account ground contact forces by a Newtonian kinematic impact- and coulomb friction law, we conducted a thorough parameter analysis based on a series of simulations. The comparison of the simulation and real world experiments shows good accordance, which qualifies the simulation for parameter optimization including prediction of robot stability and efficiency.

A dragline-forming technology has been previously proposed to enable locomotion through an open-space where no solid surfaces present. The technology is intended for situations where payload requirements are unanticipated. In those situations, variability in dragline's diameter can minimize the use of material hence increase self-sufficiency of the robot. In a previous study, a robot was designed, prototyped and proven to be able to descend through an open-space by forming a thermoplastic dragline with a diameter range of 1.1-4.5 mm. However, the speed of locomotion was rather low due to the lack of an adequate control method for thermoplastic dragline formation. In this paper, models of mass flow and thermodynamics along dragline formation pathway are presented. The models are validated in a newly prototyped robot which forms a dragline continuously. Experiment results show that, when compared to the previous prototype and control method which consists of repeated sequences of discrete events, the speed of descending locomotion is significantly increased and reaches 12.0 cm/min.

Robots composed of soft materials can achieve high deformability and conformity with unstructured and dynamic environments due to their body mechanics. However, it is challenging to gather desirable information about these robots' behaviors as conventional sensory systems are not designed to detect infinite degrees of freedom on continuum bodies. This paper presents a technical method to sensorize a soft elastic body by using conductive thermoplastic elastomer (CTPE) based strain gauge sensors. Due to its thermoplastic nature, CTPE based sensors can be fabricated in flexible sizes and shapes and integrated into the soft elastic bodies. We analyze soft elastic deformations and extract strain vectors to find finite unique regions on continuum surfaces. Then we use these regions to design the morphologies of our fiber shaped CTPE sensors to discriminate soft robotic behaviors. To demonstrate our approach and show how the programmable sensor morphologies discriminate different motion patterns, we have built a soft elastic prismatic silicone (E = 1.31MPa) block and designed two sensors to discriminate serpentine and twisting patterns.

In mammals, the developmental path that links the primary behaviours observed during foetal stages to the full fledged behaviours observed in adults is still beyond our understanding. Often theories of motor control try to deal with the process of incremental learning in an abstract and modular way without establishing any correspondence with the mammalian developmental stages. In this paper, we propose a computational model that links three distinct behaviours which appear at three different stages of development. In order of appearance, these behaviours are: spontaneous motor activity (SMA), reflexes, and coordinated behaviours, such as locomotion. The goal of our model is to address in silico four hypotheses that are currently hard to verify in vivo: First, the hypothesis that spinal reflex circuits can be self-organized from the sensor and motor activity induced by SMA. Second, the hypothesis that supraspinal systems can modulate reflex circuits to achieve coordinated behaviour. Third, the hypothesis that, since SMA is observed in an organism throughout its entire lifetime, it provides a mechanism suitable to maintain the reflex circuits aligned with the musculoskeletal system, and thus adapt to changes in body morphology. And fourth, the hypothesis that by changing the modulation of the reflex circuits over time, one can switch between different coordinated behaviours. Our model is tested in a simulated musculoskeletal leg actuated by six muscles arranged in a number of different ways. Hopping is used as a case study of coordinated behaviour. Our results show that reflex circuits can be self-organized from SMA, and that, once these circuits are in place, they can be modulated to achieve coordinated behaviour. In addition, our results show that our model can naturally adapt to different morphological changes and perform behavioural transitions.

The use of free vibration in elastic structure can lead to energy efficient robot locomotion, since it significantly reduces the energy expenditure if properly designed and controlled. However, it is not well understood how to harness the dynamics of free vibration for the robot locomotion, because of the complex dynamics originated in discrete events and energy dissipation during locomotion. From this perspective, this paper explores three minimalistic models of free vibration that can characterize the basic principle of robot locomotion. Since the robot mainly exhibits vertical hopping, three one-dimensional models are examined that contain different configurations of simple spring-damper-mass components. The self-stability of these models are also investigated in simulation. The real-world and simulation experiments show that one of the models best characterizes the robot hopping, through analyzing the basic kinematics and negative works in actuation. Based on this model, the control parameters are analyzed for the energy efficient hopping.

For more dexterous and agile legged robot locomotion, alternative actuation has been one of the most long-awaited technologies. The goal of this paper is to investigate the use of newly developed actuator, the so-called Linear Multi-Modal Actuator (LMMA), in the context of legged robot locomotion, and analyze the behavioral performance of it. The LMMA consists of three discrete couplings which enable the system to switch between different mechanical dynamics such as instantaneous switches between series elastic and fully actuated dynamics. To test this actuator for legged locomotion, this paper introduces a one-legged robot platform we developed to implement the actuator, and explains a novel control strategy for hopping, i.e. “preloaded hopping control”. This control strategy takes advantage of the coupling mechanism of the LMMA to preload the series elasticity during the flight phase to improve the energy efficiency of hopping locomotion. This paper shows a series of experimental results that compare the control strategy with a simple sinusoidal actuation strategy to discuss the benefits and challenges of the proposed approach.

The paper presents a new concept of locomotion for wheeled or legged robots through an object-free space. The concept is inspired by the behaviour of spiders forming silk threads to move in 3D space. The approach provides the possibility of variation in thread diameter by deforming source material, therefore it is useful for a wider coverage of payload by mobile robots. As a case study, we propose a technology for descending locomotion through a free space with inverted formation of threads in variable diameters. Inverted thread formation is enabled with source material thermoplastic adhesive (TPA) through thermally-induced phase transition. To demonstrate the feasibility of the technology, we have designed and prototyped a 300-gram wheeled robot that can supply and deform TPA into a thread and descend with the thread from an existing hanging structure. Experiment results suggest repeatable inverted thread formation with a diameter range of 1.1-4.5 mm, and a locomotion speed of 0.73 cm per minute with a power consumption of 2.5 W.

In recent years, there has been increasing interest in the study of gait patterns in both animals and robots, because it allows us to systematically investigate the underlying mechanisms of energetics, dexterity, and autonomy of adaptive systems. In particular, for morphological computation research, the control of dynamic legged robots and their gait transitions provides additional insights into the guiding principles from a synthetic viewpoint for the emergence of sensible self-organizing behaviors in more-degrees-of-freedom systems. This article presents a novel approach to the study of gait patterns, which makes use of the intrinsic mechanical dynamics of robotic systems. Each of the robots consists of a U-shaped elastic beam and exploits free vibration to generate different locomotion patterns. We developed a simplified physics model of these robots, and through experiments in simulation and real-world robotic platforms, we show three distinctive mechanisms for generating different gait patterns in these robots.

Despite many approaches proposed in the past, robotic climbing in a complex vertical environment is still a big challenge. We present here an alternative climbing technology that is based on thermoplastic adhesive (TPA) bonds. The approach has a great advantage because of its large payload capacity and viability to a wide range of flat surfaces and complex vertical terrains. The large payload capacity comes from a physical process of thermal bonding, while the wide applicability benefits from rheological properties of TPAs at higher temperatures and intermolecular forces between TPAs and adherends when being cooled down. A particular type of TPA has been used in combination with two robotic platforms, featuring different foot designs, including heating/cooling methods and construction of footpads. Various experiments have been conducted to quantitatively assess different aspects of the approach. Results show that an exceptionally high ratio of 500% between dynamic payloads and body mass can be achieved for stable and repeatable vertical climbing on flat surfaces at a low speed. Assessments on four types of typical complex vertical terrains with a measure, i.e., terrain shape index ranging from -0.114 to 0.167, return a universal success rate of 80%-100%.

The capability of extending body structures is one of the most significant challenges in the robotics research and it has been partially explored in self-reconfigurable robotics. By using such a capability, a robot is able to adaptively change its structure from, for example, a wheel like body shape to a legged one to deal with complexity in the environment. Despite their expectations, the existing mechanisms for extending body structures are still highly complex and the flexibility in self-reconfiguration is still very limited. In order to account for the problems, this paper investigates a novel approach to robotic body extension by employing an unconventional material called Hot Melt Adhesives (HMAs). Because of its thermo-plastic and thermo-adhesive characteristics, this material can be used for additive fabrication based on a simple robotic manipulator while the established structures can be integrated into the robot's own body to accomplish a task which could not have been achieved otherwise. This paper first investigates the HMA material properties and its handling techniques, then evaluates performances of the proposed robotic body extension approach through a case study of a “water scooping” task.

Robust climbing in unstructured environments is a long-standing challenge in robotics research. Recently there has been an increasing interest in using adhesive materials for that purpose. For example, a climbing robot using hot melt adhesives (HMAs) has demonstrated advantages in high attachment strength, reasonable operation costs, and applicability to different surfaces. Despite the advantages, there still remain several problems related to the attachment and detachment operations, which prevent this approach from being used in a broader range of applications. Among others, one of the main problems lies in the fact that the adhesive characteristics of this material were not fully understood fin the context of robotic climbing locomotion. As a result, the previous robot often could not achieve expected locomotion performances and “contaminated” the environment with HMAs left behind. In order to improve the locomotion performances, this paper focuses on attachment and detachment operations in robot climbing with HMAs. By systematically analyzing the adhesive property and bonding strength of HMAs to different materials, we propose a novel detachment mechanism that substantially improves climbing performances without HMA traces.

The adaptation of robots to changing tasks has been explored in modular self-reconfigurable robot research, where the robot structure is altered by adapting the connectivity of its constituent modules. As these modules are generally complex and large, an upper bound is imposed on the resolution of the built structures. Inspired by growth of plants or animals, robotic body extension (RBE) based on hot melt adhesives allows a robot to additively fabricate and assemble tools, and integrate them into its own body. This enables the robot to achieve tasks which it could not achieve otherwise. The RBE tools are constructed from hot melt adhesives and therefore generally small and only passive. In this paper, we seek to show physical extension of a robotic system in the order of magnitude of the robot, with actuation of integrated body parts, while maintaining the ability of RBE to construct parts with high resolution. Therefore, we present an enhancement of RBE based on hot melt adhesives with modular units, combining the flexibility of RBE with the advantages of simple modular units. We explain the concept of this new approach and demonstrate with two simple unit types, one fully passive and the other containing a single motor, how the physical range of a robot arm can be extended and additional actuation can be added to the robot body.

Due to technological limitations robot actuators are often designed for specific tasks with narrow performance goals, whereas a wide range of output and behaviours is necessary for robots to operate autonomously in uncertain complex environments. We present a design framework that employs dynamic couplings in the form of brakes and clutches to increase the performance and diversity of linear actuators. The couplings are used to switch between a diverse range of discrete modes of operation within a single actuator. We also provide a design solution for miniaturized couplings that use dry friction to produce rapid switching and high braking forces. The couplings are designed so that once engaged or disengaged no extra energy is consumed. We apply the design framework and coupling design to a linear series elastic actuator (SEA) and show that this relatively simple implementation increases the performance and adds new behaviours to the standard design. Through a number of performance tests we are able to show rapid switching between a high and a low impedance output mode; that the actuator's spring can be charged to produce short bursts of high output power; and that the actuator has additional passive and rigid modes that consume no power once activated. Robots using actuators from this design framework would see a vast increase in their behavioural diversity and improvements in their performance not yet possible with conventional actuator design.

In order to understand the underlying mechanisms of animals' agility, dexterity and efficiency in motor control, there has been an increasing interest in the study of gait patterns in biological and artificial legged systems. This paper presents a novel approach to the study of gait patterns which makes use of intrinsic mechanical dynamics of robotic systems. Each of these robots consists of a U-shape elastic beam and exploits free vibration to generate different gait patterns. We developed a conceptual model for these robots, and through simulation and real-world experiments, we show three distinct mechanisms for generating four different gait patterns in these robots.

Throwing is a complex and highly dynamic task. Humans usually exploit passive dynamics of their limbs to optimize their movement and muscle activation. In order to approach human throwing, we developed a double pendulum robotic platform. To introduce passivity into the actuated joints, clutches were included in the drive train. In this paper, we demonstrate the advantage of exploiting passive dynamics in reducing the mechanical work. However, engaging and disengaging the clutches are done in discrete fashions. Therefore, we propose an optimization approach which can deal with such discontinuities. It is shown that properly engaging/disengaging the clutches can reduce the mechanical work of a throwing task. The result is compared to the solution of fully actuated double pendulum, both in simulation and experiment.

Vertical climbing on a variety of flat surfaces with a single robot has been previously demonstrated using vacuum suction, electrostatic adhesion, and biologically inspired approaches, etc. These methods generally have a low attachment strength, and it is not clear whether they can provide satisfactory attachment on vertical terrains with richer 3D features. Recent development of a climbing technology based on hot melt adhesives (HMAs) has shown its advantage with a high attachment strength through thermal bonding and viability to any solid surfaces. However, its feasibility for vertical climbing has only been proven on flat surfaces and with external energy supplies. This paper provides quantitative measurements for vertical climbing performance on five types of surfaces and terrains with a self-contained robot exploiting HMAs. We show that robust vertical climbing on multiple terrains can be achieved with reliable high-strength attachment.

Flying insects have attracted a number of scientists for many years, and their contributions to biological sciences should never be underestimated: Research on flying insects has spread out into almost all fields, including sociology, genetics, evolutionary biology, neuroscience, and the other behavioral sciences. What I have found most fascinating about flying insects is their remarkable intelligence despite their small sizes of body and nervous structures. Flying insects live and survive almost everywhere on the planet; they are capable of wide-ranging behavioral capabilities, including basic behaviors such as takeoff, landing, escaping, chasing, and mating, as well as foraging and learning; and some species can even communicate to form social structures. While human beings exhibit similar behaviors, flying insects achieve these functions by using typically very different mechanisms from those of humans, and they are often very elegant. I don't remember how many times I was surprised by the clever solutions by which animals achieve these functionalities.Owing to the enormous amount of accumulated knowledge and the many investigations of them in biology, flying insects have also become one of the most representative model animals in engineering sciences. Previously, the visual systems of flying insects were studied intensively and incorporated into the important literature on computer vision [1, 2]. Also, flying insects were investigated as interesting case studies of mechanics, in which they revealed a number of mechanical design principles of nature [3, 4]. In robotics research, a number of interdisciplinary collaborations between engineers and biologists have successfully identified mechanisms of adaptive behaviors by sensory-motor pathways of insects [5].From what I have observed in its recent development, the research field of flying insects seems to be entering a new era based on the availability of new technologies. For behavioral studies of animals, we now have easier access to various imaging and computational devices, which facilitate more comprehensive experiments and analysis to uncover the mysteries of physiological processes and aerodynamic interactions in animals' adaptive behaviors. The rapid progress of tool availability is more prominent in robotics research: Micromanufacturing techniques as well as small-sized sensors, motors, processors, and batteries, for example, make it possible to realize remarkable robotic systems that are as small and agile as biological systems. The book Flying Insects and Robots was published at the very time when the new era of this research field had just started.The book contains 21 chapters authored by prominent experts in this rapidly growing field. Unlike similar books of this kind, which often focus exclusively on one topic, this volume covers almost all the highly active research topics investigated in the last years.The contents of this book can be roughly divided into two parts.The first half introduces the recent development of studies of sensory-motor processes in flying insects and robots. Unlike conventional robots, which typically rely on non-visual sensory information (e.g., gyros, GPS, laser range finders, and radars), vision plays a central role in the insects' flight control. The first part of this book provides the mechanisms of vision-based flight control in nature and the engineers' efforts to conceptualize these mechanisms and incorporate them into artificial systems. More specifically, this part introduces in detail the following topics on vision-based flight control.After the introductory chapter on the fundamentals of biomechanics and physiological studies of flying insects (Chapter 1), it is carefully explained how optic flow is used in flying insects (Chapters 2, 3, 6, 7, and 9). A set of more challenging problems of visuomotor pathways in the insects are also nicely summarized in this part. Namely, Chapter 4 introduces how active vision of flying insects can be studied; Chapter 5 focuses on the influence of wide-field integration in visuomotor control; and Chapter 7 introduces the recent model-based studies of insects' visual navigation.Compared to other such books, a unique aspect of this one lies in the fact that almost all chapters explain how the biological studies have led to engineering contributions. For example, Chapters 2 and 3 introduce how the biological studies on optic flow resulted in robust speed control and obstacle avoidance of aerial vehicles, and Chapter 9 explains how the basic principles of visual odometry can be engineered and integrated into a robotic system. Chapters 8 and 10 suggest how the cutting-edge small-scale manufacturing techniques could be employed to reproduce biomimetic visual sensory systems.The second topic area of this book is the flight mechanisms of small-sized flying robots. Throughout 11 chapters (Chapters 11–21), this book documents the engineers' ceaseless challenges in designing animal-like flying robots. More specifically, Chapters 11 and 12 explain the underlying aerodynamics of both flapping and fixed wings for micro aerial vehicles (MAVs), and Chapters 13 and 14 introduce the challenges of flapping wing designs that exploit passive dynamics. The design issues are then extended to the whole body in Chapters 15, 16, 18, and 19, which discuss how motors and other components could be integrated into small body structures to achieve many motor functions (including flying, jumping, and walking). Chapter 17 covers the fundamentals of motion control in flying insects. And the last two chapters introduce the important technological challenges to solar-powered MAVs (Chapter 20) and microfabrication techniques (Chapter 21).Because MAVs are severely constrained by body size, body weight, actuation power, energy storage, and computational resources, the development of these robots is highly challenging. The highlight of this book, at least from my perspective, is the ways researchers learn abstract design principles from nature and incorporate them into functional organisms. Such understanding-by-building approaches can be best represented, for example, by the discovery of mechanically self-stabilizing passive mechanisms (e.g., Chapters 13 and 15) and minimalistic control architectures (Chapters 17, 18, and 19).Because this book introduces the active research areas and the current technical challenges one by one in great detail, each chapter is self-contained and there is relatively small coherence between chapters. For example, the issue of optic flow is explained from different perspectives in five or six chapters, and many design challenges of micro aerial vehicles are also introduced in five or six different chapters. Thus, for those who expect comprehensive introductory lessons, it will be difficult to grasp the big picture from this book. This volume is not a textbook for non-experts that explains an established discipline systematically and concisely, but is intended to provide an overview of ongoing research projects. For this reason, it might be helpful to go through some complementary materials (e.g., [6, 7]) along with this book, which provide some underlying guiding principles of bio-inspired robotics.It is also important to mention that this volume has several faces. For biologists, it should be interesting to see the collections of success stories in which biological studies and findings were transferred to engineering sciences and applications; researchers in microfabrication and/or unconventional materials will find a number of challenges and applications that they could contribute to; and scientists in computer engineering and optics will obtain additional insights into the nature of sensory-motor processes. All in all, because of its interdisciplinary character, it should not be difficult for most scientists to find a way to enjoy this book.In general, the book contains an excellent up-to-date collection of research projects in flying insects and robots. The recent achievements and challenges are introduced by the leading scientists of the field, and each chapter is self-contained, which may be highly beneficial for those who are interested in specific areas of research. The target audience should be both engineering and biological scientists, including mechanical, electrical, and computational engineering as well as artificial life/intelligence, neuroscience, and other behavioral sciences. I would also recommend the use of this book in advanced courses of graduate studies.

Robust climbing in unstructured environment has been one of the long-standing challenges in robotics research. Among others, the control of large adhesion forces is still an important problem that significantly restricts the locomotion performance of climbing robots. The main contribution of this paper is to propose a novel approach to autonomous robot climbing which makes use of Hot Melt Adhesion (HMA). The HMA material is known as a very economical solution to achieve large adhesion forces, and the forces can be varied by controlling its material temperature. For locomotion in both inclined and vertical walls, this paper investigates the basic characteristics of HMA material, and proposes a design and control of climbing robot that uses the HMA material for attaching and detaching its body to the environment. The robot is equipped with servomotors and thermal control units to actively vary the temperature of the material, and the coordination of these components enables the robot to walk against the gravitational forces even with a relatively large body weight. A real-world platform is used to demonstrate locomotion on a vertical wall, and the experimental result explains feasibility and overall performances of this approach.

Personal robots anticipated to become popular in the future are required to be active in joint work and community life with humans. These personal robots must recognize changing environment and must conduct adequate actions like human. Visual tracking can be said as a fundamental function from the view point of environmental sensing and reflex reaction against it. The authors developed a visual tracking motion algorithm by using upper body. Then, we integrated it with an online walking pattern generator and developed a visual tracking biped locomotion. Finally, we conducted an experimental evaluation with emotion expression.

Personal robots and robot technology (RT)-based assistive devices are expected to play a major role in our elderly-dominated society, by interacting with surrounding people both physically and psychologically. A fundamental role during the interaction is of course played by the hand. In this paper we present the evaluation of the effect of hand shape to the quality of the interaction, in particular during handshake.

Although there has been an increasing interest in dynamic bipedal locomotion for significant improvement of energy efficiency and dexterity of mobile robots in the real world, their locomotion capabilities are still mostly restricted on flat surfaces. The difficulty of dynamic locomotion in rough terrain is mainly originated in the stability and controllability of gait patterns while exploiting the natural mechanical dynamics of the robots. For a systematic investigation of the challenging problem, this paper presents the simplest control architecture for the compass gait model which can be used for locomotion in rough terrain. Locomotion of the model is mainly achieved by an open-loop oscillator which induces self-stabilizing gait patterns, and we test the proposed control architecture in a real-world robotic platform. In addition, we also found that this controller is capable of varying stride length with a minimum change of control parameters, which enables locomotion in rough terrains. By using these basic principles of self-stability and gait variability, we extended the proposed controller with a simple sensory feedback about the location in the environment, which makes the robot possible to control gait patterns autonomously for traversing a rough terrain. We describe a set of experimental results and discuss how the proposed minimalistic control architecture can be enhanced for dynamic locomotion control in more complex environment.

Personal robots and robot technology (RT)-based assistive devices are expected to play a major role in our elderly-dominated society, with an active participation to joint works and community life with humans, as partner and as friends for us. In particular, these robots are expected to be fundamental for helping and assisting elderly and disabled people during their activities of daily living (ADLs). To achieve this result, personal robots should be also capable of human-like emotion expressions. To this purpose we developed a new whole body emotion expressing bipedal humanoid robot, named KOBIAN, which is also capable to express human-like emotions. In this paper we presented three different evaluations of the emotional expressiveness of KOBIAN. In particular In particular, we presented the analysis of the roles of the face, the body, and their combination in emotional expressions. We also compared Emotional patterns created by a Photographer and a Cartoonist with the ones created by us. Overall, although the experimental results are not as good as we were expecting, we confirmed the robot can clearly express its emotions, and that very high recognition ratios are possible.

In human and animal running spring-like leg behavior is found, and similar concepts have been demonstrated by various robotic systems in the past. In general, a spring-mass model provides self-stabilizing characteristics against external perturbations originated in leg-ground interactions and motor control. Although most of these systems made use of linear spring-like legs. The question addressed in this paper is the influence of leg segmentation (i.e. the use of rotational joint and two limb-segments) to the self-stability of running, as it appears to be a common design principle in nature. This paper shows that, with the leg segmentation, the system is able to perform self-stable running behavior in significantly broader ranges of running speed and control parameters (e.g. control of angle of attack at touchdown, and adjustment of spring stiffness) by exploiting a nonlinear relationship between leg force and leg compression. The concept is investigated by using a two- segment leg model and a robotic platform, which demonstrate the plausibility in the real world.

Passive dynamics plays an important role in legged locomotion of the biological systems. The use of passive dynamics provides a number of advantages in legged locomotion such as energy efficiency, self-stabilization against disturbances, and generating gait patterns and behavioral diversity. Inspired from the theoretical and experimental studies in biomechanics, this paper presents a novel bipedal locomotion model for walking and running behavior which uses compliant legs. This model consists of three-segment legs, two servomotors, and four passive joints that are constrained by eight tension springs. The self-organization of two gait patterns (walking and running) is demonstrated in simulation and in a real-world robot. The analysis of joint kinematics and ground reaction force explains how a minimalistic control architecture can exploit the particular leg design for generating different gait patterns. Moreover, it is shown how the proposed model can be extended for controlling locomotion velocity and gait patterns with the simplest control architecture.

While underactuated robotic systems are capable of energy efficient and rapid dynamic behavior, we still do not fully understand how body dynamics can be actively used for adaptive behavior in complex unstructured environment. In particular, we can expect that the robotic systems could achieve high maneuverability by flexibly storing and releasing energy through the motor control of the physical interaction between the body and the environment. This paper presents a minimalistic optimization strategy of motor control policy for underactuated legged robotic systems. Based on a reinforcement learning algorithm, we propose an optimization scheme, with which the robot can exploit passive elasticity for hopping forward while maintaining the stability of locomotion process in the environment with a series of large changes of ground surface. We show a case study of a simple one-legged robot which consists of a servomotor and a passive elastic joint. The dynamics and learning performance of the robot model are tested in simulation, and then transferred the results to the real-world robot.

October 01 2007 Autonomous Robots: From Biological Inspiration to Implementation and Control. George A. Bekey. (2005, MIT Press.) Hardcover, 577 pages. ISBN 0262025787 Fumiya Iida Fumiya Iida Search for other works by this author on: This Site Google Scholar Author and Article Information Fumiya Iida Online Issn: 1530-9185 Print Issn: 1064-5462 © 2007 Massachusetts Institute of Technology2007 Artificial Life (2007) 13 (4): 419–421. https://doi.org/10.1162/artl.2007.13.4.419 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Fumiya Iida; Autonomous Robots: From Biological Inspiration to Implementation and Control. George A. Bekey. (2005, MIT Press.) Hardcover, 577 pages. ISBN 0262025787. Artif Life 2007; 13 (4): 419–421. doi: https://doi.org/10.1162/artl.2007.13.4.419 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2007 Massachusetts Institute of Technology2007 Article PDF first page preview Close Modal You do not currently have access to this content.

There is much to gain from providing walking machines with passive dynamics, e.g. by including compliant elements in the structure. These elements can offer interesting properties such as self-stabilization, energy efficiency and simplified control. However, there is still no general design strategy for such robots and their controllers. In particular, the calibration of control parameters is often complicated because of the highly nonlinear behavior of the interactions between passive components and the environment. In this article, we propose an approach in which the calibration of a key parameter of a walking controller, namely its intrinsic frequency, is done automatically. The approach uses adaptive frequency oscillators to automatically tune the intrinsic frequency of the oscillators to the resonant frequency of a compliant quadruped robot. The tuning goes beyond simple synchronization and the learned frequency stays in the controller when the robot is put to halt. The controller is model free, robust and simple. Results are presented illustrating how the controller can robustly tune itself to the robot, as well as readapt when the mass of the robot is changed. We also provide an analysis of the convergence of the frequency adaptation for a linearized plant, and show how that analysis is useful for determining which type of sensory feedback must be used for stable convergence. This approach is expected to explain some aspects of developmental processes in biological and artificial adaptive systems that "develop" through the embodied system-environment interactions

New robotics is an approach to robotics that, in contrast to traditional robotics, employs ideas and principles from biology. While in the traditional approach there are generally accepted methods (e. g., from control theory), designing agents in the new robotics approach is still largely considered an art. In recent years, we have been developing a set of heuristics, or design principles, that on the one hand capture theoretical insights about intelligent (adaptive) behavior, and on the other provide guidance in actually designing and building systems. In this article we provide an overview of all the principles but focus on the principles of ecological balance, which concerns the relation between environment, morphology, materials, and control, and sensory-motor coordination, which concerns self-generated sensory stimulation as the agent interacts with the environment and which is a key to the development of high-level intelligence. As we argue, artificial evolution together with morphogenesis is not only "nice to have" but is in fact a necessary tool for designing embodied agents.

In this paper a new kind of hopping robot has been designed which uses inverse pendulum dynamics to induce bipedal hopping gaits. Its mechanical structure consists of a rigid inverted T-shape mounted on four compliant feet. An upright "T" structure is connected to this by a rotary joint. The horizontal beam of the upright "T" is connected to the vertical beam by a second rotary joint. Using this two degree of freedom mechanical structure, with simple reactive control, the robot is able to perform hopping, walking and running gaits. During walking, it is experimentally shown that the robot can move in a straight line, reverse direction and control its turning radius. The results show that such a simple but versatile robot displays stable locomotion and can be viable for practical applications on uneven terrain.