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Recent results suggest that compliance and non-linearity in physical bodies of soft robots may not be disadvantageous properties with respect to control, but rather of advantage. In the context of morphological computation one could see such complex structures as potential computational resources. In this study, we implement and exploit this view point in a spine-driven quadruped robot called Kitty by using its flexible spine as a computational resource. The spine is an actuated multi-joint structure consisting of a sequence of soft silicone blocks. Its complex dynamics are captured by a set of force sensors and used to construct a closed-loop to drive the motor commands. We use simple static, linear readout weights to combine the sensor values to generate multiple gait patterns (bounding, trotting, turning behavior). In addition, we demonstrate the robustness of the setup by applying strong external perturbations in form of additional loads. The system is able to fully recover to its nominal gait patterns (which are encoded in the linear readout weights) after the perturbation has vanished. [ABSTRACT FROM PUBLISHER]

Aim at enhancing dexterous and safe operation in unstructured environment, a cable-driven soft robotic manipulator is designed in this paper. Due to soft material it made of and nearly infinite degree of freedom it owns, the soft robotic manipulator has higher security and dexterity than traditional rigid-link manipulator, which make it suitable to perform tasks in complex environments that is narrow, confined and unstructured. Though the soft robotic manipulator possesses advantages above, it is not an easy thing for it to achieve precise position control. In order to solve this problem, a kinematic model based on piecewise constant curvature hypothesis is proposed. Through building up three spaces and two mappings, the relationship between the length variables of 4 cables and the position and orientation of the soft robotic manipulator end-effector is obtained. Afterwards, a depth-independent image Jacobian matrix is introduced and an image-based visual servo controller is presented. Applied by adaptive algorithm, the controller could estimate unknown position of the feature point online, and then Lyapunov theory is used to prove the stability of the proposed controller. At last, experiments are conducted to demonstrate rationality and validity of the kinematic model and adaptive visual servo controller. [ABSTRACT FROM PUBLISHER]

The biological hypothesis of spinal engine states that locomotion is mainly achieved by the spine, while the legs may serve as assistance. Inspired by this hypothesis, a compliant, multiple degree-of-freedom, biologically-inspired spine has been embedded into a quadruped robot, named Kitty, which has no actuation on the legs. In this paper, we demonstrate how versatile behaviors (bounding, trotting, and turning) can be generated exclusively by the spine's movements through dynamical interaction between the controller, the body, and the environment, known as embodiment. Moreover, we introduce information theoretic approach to quantitatively study the spine internal dynamics and its effect on the bounding gait based on three spinal morphologies. These three morphologies differ in the position of virtual spinal joint where the spine is easier to get bent. The experimental results reveal that locomotion can be enhanced by using the spine featuring a rear virtual spinal joint, which offers more freedom for the rear legs to move forward. In addition, the information theoretic analysis shows that, according to the morphological differences of the spine, the information structure changes. The relationship between the observed behavior of the robot and the corresponding information structure is discussed in detail. [ABSTRACT FROM PUBLISHER]

Soft robots have significant advantages over traditional robots made of rigid materials. However, controlling this type of robot by conventional approaches is difficult. Reservoir computing has been demonstrated to be an effective approach for achieving rapid learning in benchmark tasks and conventional robots. In this study, we investigated the feasibility and capacity of the reservoir computing approach to embedding and switching between multiple behaviors in a on-line manner in a soft robotic arm. The result shows that this approach can successfully achieve this task. [ABSTRACT FROM PUBLISHER]

Stable grasp is the result of sensorimotor regulation of forces, ensuring sufficient grip force and the integrity of the held object. Grasping with a prosthesis introduces the challenge of finding the appropriate forces given the engineered sensorimotor prosthetic interface. Excessive force leads to unnecessary energy use and possible damage to the object. In contrast, low grip forces lead to slippage. In order for a prosthetic hand to achieve a stable grasp, the haptic information provided to the prosthesis wearer needs to display these two antagonistic grasp metrics (force and slip) in a quantified way. We present the design and evaluation of a wearable single-actuator haptic device that relays multi-modal haptic information, such as grip force and slip speed. Two belts that are activated in a mutually exclusive manner by the rotation direction of a single motor exert normal force and tangential motion on the skin surface, respectively. The wearable haptic device is able to display normal forces as a tap frequency in the range of approximately 1.5–5.0 Hz and slip speed in the range of 50–200 mm/s. Within these values, users are able to identify at least four stimulation levels for each feedback modality, with short-term training. [ABSTRACT FROM PUBLISHER]

Soft robots have significant advantages over traditional rigid robots because of their morphological flexibility. However, the use of conventional engineering approaches to control soft robots is difficult, especially to achieve autonomous behaviors. With its completely soft body, the octopus has a rich behavioral repertoire, so it is frequently used as a model in building and controlling soft robots. However, the sensorimotor control strategies in some interesting behaviors of the octopus, such as octopus crawling, remain largely unknown. In this study, we review related biological studies on octopus crawling behavior and propose its sensorimotor control strategy. The proposed strategy is implemented with an echo state network on an octopus-inspired, multi-arm crawling robot. We also demonstrate the control strategy in the robot for autonomous direction and speed control. Finally, the implications of this study are discussed. [ABSTRACT FROM PUBLISHER]

Recently, the information theoretic approach has been increasingly used in the robotics community as powerful quantitative measures for characterizing the dynamic coupling between the controller, the body, and the environment in embodied robots. This approach is effective and useful even if this interaction regime becomes complex and nonlinear as is often the case in soft robots. In this study, we propose a method for characterizing and visualizing the information transfer spa-tiotemporally through the robot's body. This method is based on the framework called “local information transfer” proposed by Lizier et al. We extend it with the permutation-information theoretic approach, which makes it feasible for continuous time series data usually obtained in robotic platforms. To test the power of the proposed method, we performed experiments using a soft robotic arm simulator and a silicone-based soft robotic arm platform inspired by the octopus and showed that the external damage spreading is successfully and clearly visualized by the method. We also analyzed the robustness of the method to noise. Finally, we discuss future applications and possible extensions. [ABSTRACT FROM PUBLISHER]

In quadruped animals, spinal movements contribute to locomotion in terms of controlling body posture, providing the foundation to generate leg movement, and integrating limb and trunk actions. Inspired by this biological findings, we develop two quadruped models featuring different numbers of spinal joints to demonstrate the spine-driven locomotion behaviors. To gain a deep understanding of how the locomotion is achieved by axial driven propulsion and how the spinal morphology affects locomotion, we exclusively employ actuated spinal joint(s) to the model with a minimalistic control strategy. We choose three individuals from these two models and analyze their behaviors in terms of gait properties, i.e., angle of attack, ground clearance, and movement of the center of mass. The results show that employing the spinal morphology with two joints can greatly enhance the stability and speed of locomotion. Among several advantageous properties of the two spinal joint model we identify the following. First, it allows the robot to adjust the movement of the center of mass to stabilize itself. Second, by providing more freedom to bend the spine, the robot can pull the rear legs forward, thus increasing the stride length. Finally, locomotion with this model exhibits two flight phases and greater flight proportion during each stride, similar to what it is observed from running cheetahs, which make significant difference in the speed and the gait. [ABSTRACT FROM PUBLISHER]

Soft robots are difficult to control because of their compliant and elastic body dynamics compared with robots made of rigid bodies. In this paper, we present a control scheme inspired by the octopus called timing-based control for soft robotic arms. This control scheme is motivated to positively exploit the natural dynamics of the soft body. We demonstrate a scheme for controlling an object-reaching task by using an echo state network on a 3D physical soft robotic arm simulator and show that this network can successfully perform the task. Detailed analyses and evaluations of the generalization capacity of the network and the performances to the reaching task are presented. [ABSTRACT FROM PUBLISHER]

Recent bio-mimetic robotics and embodied intelligence research have revealed the importance of reciprocal and dynamical coupling between the brain (controller), the body, and the environment. A typical example of this is a soft robot that has a diversity of compliant and elastic body dynamics. Coupling between the environment and the controller is expected to be enhanced because of the softness of the robot's body. Accordingly, there exists an increasing demand for a method to quantitatively and effectively characterize such coupling regimes. In this paper, we show that the information theoretic approach can be effectively used for this purpose. By using a simple soft robotic platform inspired by the morphology and the material property of an octopus, for example, we analyzed these characteristics and revealed that, because of the compliant and elastic body dynamics, our soft robotic arm was highly sensitive to environmental change. [ABSTRACT FROM PUBLISHER]