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.

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.

Mobile robots suffer from sensory data corruption due to body oscillations and disturbances. In particular, information loss on images captured with onboard cameras can be very high, and such loss may become irreversible or computationally costly to undo. In this paper, we propose a novel method to minimize average motion blur captured by such mobile visual sensors. To this end, we derive a motion blur metric (MMBM) that can be computed in real-time by using only inertial sensor measurements and validate it through comparisons with optic flow computations. The applicability of MMBM is illustrated through a motion blur minimizing system implemented on the SensoRHex hexapod robot by externally triggering an onboard camera based on MMBM values computed in real-time while the robot is walking straight on a flat surface. The resulting motion blur is compared to motion blur levels obtained with a regular, fixed frame-rate image acquisition schedule by both qualitative inspection and using a blind blur metric on captured images. MMBM based motion blur minimization system not only reduces average motion blur, but also avoids frames with extreme motion blur before an image gets corrupted by appropriately delaying the triggering of frame acquisition.