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Bioinspired and biohybrid robots can help respond to diverse, sustainable application needs., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

The soft capabilities of biological appendages like the arms of Octopus vulgaris and elephants' trunks have inspired roboticists to develop their robotic equivalents. Although there have been considerable efforts to replicate their morphology and behavior patterns, we are still lagging behind in replicating the dexterity and efficiency of these biological systems. This is mostly due to the lack of development and application of dynamic controllers on these robots which could exploit the morphological properties that a soft-bodied manipulator possesses. The complexity of these high-dimensional nonlinear systems has deterred the application of traditional model-based approaches. This paper provides a machine learning-based approach for the development of dynamic models for a soft robotic manipulator and a trajectory optimization method for predictive control of the manipulator in task space. To the best of our knowledge this is the first demonstration of a learned dynamic model and a derived task space controller for a soft robotic manipulator. The validation of the controller is carried out on an octopus-inspired soft manipulator simulation derived from a piecewise constant strain approximation and then experimentally on a pneumatically actuated soft manipulator. The results indicate that such an approach is promising for developing fast and accurate dynamic models for soft robotic manipulators while being applicable on a wide range of soft manipulators.

We introduce an octopus-inspired, underwater, soft-bodied robot capable of performing waterborne pulsed-jet propulsion and benthic legged-locomotion. This vehicle consists for as much as 80% of its volume of rubber-like materials so that structural flexibility is exploited as a key element during both modes of locomotion. The high bodily softness, the unconventional morphology and the non-stationary nature of its propulsion mechanisms require dynamic characterization of this robot to be dealt with by ad hoc techniques. We perform parameter identification by resorting to a hybrid optimization approach where the characterization of the dual ambulatory strategies of the robot is performed in a segregated fashion. A least squares-based method coupled with a genetic algorithm-based method is employed for the swimming and the crawling phases, respectively. The outcomes bring evidence that compartmentalized parameter identification represents a viable protocol for multi-modal vehicles characterization. However, the use of static thrust recordings as the input signal in the dynamic determination of shape-changing self-propelled vehicles is responsible for the critical underestimation of the quadratic drag coefficient.

Cephalopods (i.e., octopuses and squids) are being looked upon as a source of inspiration for the development of unmanned underwater vehicles. One kind of cephalopod-inspired soft-bodied vehicle developed by the authors entails a hollow, elastic shell capable of performing a routine of recursive ingestion and expulsion of discrete slugs of fluids which enable the vehicle to propel itself in water. The vehicle performances were found to depend largely on the elastic response of the shell to the actuation cycle, thus motivating the development of a coupled propulsion-elastodynamics model of such vehicles. The model is developed and validated against a set of experimental results performed with the existing cephalopod-inspired prototypes. A metric of the efficiency of the propulsion routine which accounts for the elastic energy contribution during the ingestion/expulsion phases of the actuation is formulated. Demonstration on the use of this model to estimate the efficiency of the propulsion routine for various pulsation frequencies and for different morphologies of the vehicles are provided. This metric of efficiency, employed in association with the present elastodynamics model, provides a useful tool for performing a priori energetic analysis which encompass both the design specifications and the actuation pattern of this new kind of underwater vehicle.

This paper studies underwater legged locomotion (ULL) by means of a robotic octopus-inspired prototype and its associated model. Two different types of propulsive actions are embedded into the robot model: reaction forces due to leg contact with the ground and hydrodynamic forces such as the drag arising from the sculling motion of the legs. Dynamic parameters of the model are estimated by means of evolutionary techniques and subsequently the model is exploited to highlight some distinctive features of ULL. Specifically, the separation between the center of buoyancy (CoB)/center of mass and density affect the stability and speed of the robot, whereas the sculling movements contribute to propelling the robot even when its legs are detached from the ground. The relevance of these effects is demonstrated through robotic experiments and model simulations; moreover, by slightly changing the position of the CoB in the presence of the same feed-forward activation, a number of different behaviors (i.e. forward and backward locomotion at different speeds) are achieved.

This work addresses the inverse kinematics problem of a bioinspired octopus-like manipulator moving in three-dimensional space. The bioinspired manipulator has a conical soft structure that confers the ability of twirling around objects as a real octopus arm does. Despite the simple design, the soft conical shape manipulator driven by cables is described by nonlinear differential equations, which are difficult to solve analytically. Since exact solutions of the equations are not available, the Jacobian matrix cannot be calculated analytically and the classical iterative methods cannot be used. To overcome the intrinsic problems of methods based on the Jacobian matrix, this paper proposes a neural network learning the inverse kinematics of a soft octopus-like manipulator driven by cables. After the learning phase, a feed-forward neural network is able to represent the relation between manipulator tip positions and forces applied to the cables. Experimental results show that a desired tip position can be achieved in a short time, since heavy computations are avoided, with a degree of accuracy of 8% relative average error with respect to the total arm length.

Animals exploit soft structures to move effectively in complex natural environments. These capabilities have inspired robotic engineers to incorporate soft technologies into their designs. The goal is to endow robots with new, bioinspired capabilities that permit adaptive, flexible interactions with unpredictable environments. Here, we review emerging soft-bodied robotic systems, and in particular recent developments inspired by soft-bodied animals. Incorporating soft technologies can potentially reduce the mechanical and algorithmic complexity involved in robot design. Incorporating soft technologies will also expedite the evolution of robots that can safely interact with humans and natural environments. Finally, soft robotics technology can be combined with tissue engineering to create hybrid systems for medical applications., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Soft robotics is a challenging and promising branch of robotics. It can drive significant improvements across various fields of traditional robotics, and contribute solutions to basic problems such as locomotion and manipulation in unstructured environments. A challenging task for soft robotics is to build and control soft robots able to exert effective forces. In recent years, biology has inspired several solutions to such complex problems. This study aims at investigating the smart solution that the Octopus vulgaris adopts to perform a crawling movement, with the same limbs used for grasping and manipulation. An ad hoc robot was designed and built taking as a reference a biological hypothesis on crawling. A silicone arm with cables embedded to replicate the functionality of the arm muscles of the octopus was built. This novel arm is capable of pushing-based locomotion and object grasping, mimicking the movements that octopuses adopt when crawling. The results support the biological observations and clearly show a suitable way to build a more complex soft robot that, with minimum control, can perform diverse tasks.

Anticipation of sensory consequences of actions is critical for the predictive control of movement that explains most of our sensory-motor behaviors. Plenty of neuroscientific studies in humans suggest evidence of anticipatory mechanisms based on internal models. Several robotic implementations of predictive behaviors have been inspired on those biological mechanisms in order to achieve adaptive agents. This paper provides an overview of such neuroscientific and robotic evidences; a high-level architecture of sensory-motor coordination based on anticipatory visual perception and internal models is then introduced; and finally, the paper concludes by discussing the relevance of the proposed architecture within the context of current research in humanoid robotics.

This paper reports the rationale and design of a robotic arm, as inspired by an octopus arm. The octopus arm shows peculiar features, such as the ability to bend in all directions, to produce fast elongations, and to vary its stiffness. The octopus achieves these unique motor skills, thanks to its peculiar muscular structure, named muscular hydrostat. Different muscles arranged on orthogonal planes generate an antagonistic action on each other in the muscular hydrostat, which does not change its volume during muscle contractions, and allow bending and elongation of the arm and stiffness variation. By drawing inspiration from natural skills of octopus, and by analysing the geometry and mechanics of the muscular structure of its arm, we propose the design of a robot arm consisting of an artificial muscular hydrostat structure, which is completely soft and compliant, but also able to stiffen. In this paper, we discuss the design criteria of the robotic arm and how this design and the special arrangement of its muscular structure may bring the building of a robotic arm into being, by showing the results obtained by mathematical models and prototypical mock-ups.

The present paper aims at understanding the biomechanics of an octopus tentacle as preliminary work for designing and developing a new robotic octopus tentacle. The biomechanical characterization of the biological material has been carried out on samples of Octopus vulgaris tentacles with engineering methods and tools, i.e. by biomechanical measurements of the tentacle elasticity and tension-compression stress/stretch curves. Another part of the activities has been devoted to the study of materials that can reproduce the viscoelastic behavior of the tentacle. The work presented here is part of the ongoing study and analysis on new design principles for actuation, sensing, and manipulation control, for robots with increased performance, in terms of dexterity, control, flexibility, applicability.