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.

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.

Gait emergence and adaptation in animals is unmatched in robotic systems. Animals can create and recover locomotive functions "on-the-fly" after an injury whereas locomotion controllers for robots lack robustness to morphological changes. In this work, we extend previous research on emergent interlimb coordination of legged robots based on coupled phase oscillators with force feedback terms. We investigate how the coupling weights between these phase oscillators can be extracted from the morphology with a fast and computationally lightweight method based on a combination of twitching and Hebbian learning to form sensor-motor maps. The coefficients of these maps create naturally scaled weights, which not only lead to robust gait limit cycles, but can also adapt to morphological modifications such as sensor loss and limb injuries within a few gait cycles. We demonstrate the approach on a robotic quadruped and hexapod.

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.

Jamming of granular media has been shown to possess the property of stiffness variation, transitioning from a soft to a quasi-solid state depending on the packing density of the granules. Recently, a gradual stiffness change for bending has been reported by using compliant, cubic shaped granules. Here we demonstrate that the same method and material also exhibits a gradual stiffness change for compression. As a potential application of “compliant jamming”, a bio-inspired robotic platform with jamming membranes as end effectors is designed and tasked with climbing straight vertical shafts. First, the benefit of varying the bending stiffness is investigated in the climbing task by measuring the friction force that the end effectors are applying to the shaft walls, especially if the walls are irregularly shaped. Then the role of compressive stiffness variation is explored by analyzing the performance of the robot in the climbing task, showing multi-modal properties of jamming membranes: (i) enabling a pressure sensor to detect the shaft walls, acting (ii) as grippers that actively use the irregularities of the walls to climb up by state-switching the granular material and (iii) as force dissipators that can dissipate internal forces caused by closed kinematic chains.

One of the main strengths of self-reconfigurable modular robots (SRMR) is their ability to shape-shift and dynamically change their morphology. In the case of our SRMR system “Roombots”, these shapes can be quite arbitrary for a great variety of tasks while the major utility is envisioned to be self-reconfigurable furniture. As such, the ideas and inspirations from users quickly need to be translated into the final Roombots shape. This involves a multitude of separate processes and - most importantly - requires an intuitive user interface. Our current approach led to the development of a tangible user interface (TUI) which involves 3D-scanning of a shape formed by modeling clay and the necessary steps to prepare the digitized model to be formed by Roombots. The system is able to generate a solution in less than two minutes for our target use as demonstrated with various examples.

Animal locomotion exhibits all the features of complex non linear systems such as multi-stability, critical fluctuation, limit cycle behavior and chaos. Studying these aspects on real robots has been proved difficult and therefore results mostly rely on the use of computer simulation. Simple control approaches - based on phase oscillators - have been proposed and exhibit several of these features. In this work, we compare two types of controllers: (a) an open loop control approach based on phase oscillators and (b) the Tegotae-based closed loop extension of this controller. The first controller has been shown to exhibit synchronization features between the body and the controller when applied to a quadruped robot with compliant leg structures. In this contribution, we apply both controllers to the locomotion of a stiff quadruped structure. We show that the Tegotae-controller exhibits self-organizing behavior, such as spontaneous gait transition and critical fluctuation. Moreover, it exhibits features such as the ability to stabilize both asymmetric and symmetric morphological changes, despite the lack of compliance in the leg.

Self-reconfigurable modular robots (SRMR) offer high flexibility in task space by adopting different morphologies for different tasks. Using the same simple module, complex and more capable morphologies can be built. However, increasing the number of modules increases the degrees of freedom (DOF) of the system. Thus, controlling the system as a whole becomes harder. Indeed, even a 10 DOFs system is difficult to consider and manipulate. Intuitive and easy to use interfaces are needed, particularly when modular robots need to interact with humans. In this study we present an interface to assemble desired structures and placement of such structures, with a focus on the assembly process. Roombots modules, a particular SRMR design, are used for the demonstration of the proposed interface. Two non-conventional input/output devices - a head mounted display and hand tracking system - are added to the system to enhance the user experience. Finally, a user study was conducted to evaluate the interface. The results show that most users enjoyed their experience. However, they were not necessarily convinced by the gesture control, most likely for technical reasons.

Roombots (RB) are self-reconfigurable modular robots designed to explore physical structure change by robotic reconfiguration and adaptive locomotion on structured grid environments or unstructured environments. The primary goal of RB is to create adaptive furniture. In this study, we propose a novel and user-friendly interface to control position and intensity of a mobile desk light using RB modules. In the proposed method, the user interacts with the RB with only hand/arm gestures. The user's arm is tracked with a single Kinect having bird's eye view. We demonstrate the effectiveness of the proposed interface in real hardware setup and discuss contributions of it.