Soft robotics is a growing area of research which utilizes the compliance and adaptability of soft structures to develop highly adaptive robotics for soft interactions. One area in which soft robotics has the ability to make significant impact is in the development of soft grippers and manipulators. With an increased requirement for automation, robotics systems are required to perform task in unstructured and not well defined environments; conditions which conventional rigid robotics are not best suited. This requires a paradigm shift in the methods and materials used to develop robots such that they can adapt to and work safely in human environments. One solution to this is soft robotics, which enables soft interactions with the surroundings while maintaining the ability to apply significant force. This review paper assesses the current materials and methods, actuation methods and sensors which are used in the development of soft manipulators. The achievements and shortcomings of recent technology in these key areas are evaluated, and this paper concludes with a discussion on the potential impacts of soft manipulators on industry and society.

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.

Feline locomotion combines great acrobatic proficiency, unparalleled balance and higher accelerations than other animals. Capable of accelerating from 0 to 100 km h −1 in three seconds, the cheetah (Acinonyx jubatus) is still a mystery which intrigues scientists. Aiming for a better understanding of the source of such higher speeds, we develop a biomimetic platform, where musculoskeletal parameters (range of motion and moment arms) from the biological system can be evaluated with air muscles within a lightweight robotic structure. We performed experiments validating the muscular structure during a treadmill walk, successfully reproducing animal locomotion while adopting an EMG based control method.

Conventionally, robot morphologies are developed through simulations and calculations, and different control methods are applied afterwards. Assuming that simulations and predictions are simplified representations of our reality, how sure can roboticists be that the chosen morphology is the most adequate for the possible control choices in the real-world? Here we study the influence of the design parameters in the creation of a robot with a Bayesian morphology-control (MC) co-optimization process. A robot autonomously creates child robots from a set of possible design parameters and uses Bayesian Optimization (BO) to infer the best locomotion behavior from real world experiments. Then, we systematically change from an MC co-optimization to a control-only (C) optimization, which better represents the traditional way that robots are developed, to explore the trade-off between these two methods. We show that although C processes can greatly improve the behavior of poor morphologies, such agents are still outperformed by MC co-optimization results with as few as 25 iterations. Our findings, on one hand, suggest that BO should be used in the design process of robots for both morphological and control parameters to reach optimal performance, and on the other hand, point to the downfall of current design methods in face of new search techniques.

In this paper, we describe the development of the quadruped robot "Ken" with the minimalistic and lightweight body design for achieving fast locomotion. We use McKibben pneumatic artificial muscles as actuators, providing high frequency and wide stride motion of limbs, also avoiding problems with overheating. We conducted a preliminary experiment, finding out that the robot can swing its limb over 7.5 Hz without amplitude reduction, nor heat problems. Moreover, the robot realized a several steps of bouncing gait by using simple CPG-based open loop controller, indicating that the robot can generate enough torque to kick the ground and limb contraction to avoid stumbling.

Studies on decerebrate walking cats have shown that phase transition is strongly related to muscular sensory signals at limbs. To further investigate the role of such signals terminating the stance phase, we developed a biomimetic feline platform. Adopting link lengths and moment arms from an Acinonyx jubatus, we built a pair of hindlimbs connected to a hindquarter and attached it to a sliding strut, simulating solid forelimbs. Artificial pneumatic muscles simulate biological muscles through a control method based on EMG signals from walking cats (Felis catus). Using the bio-inspired muscular unloading rule, where a decreasing ground reaction force triggers phase transition, stable walking on a treadmill was achieved. Finally, an alternating gait is possible using the unloading rule, withstanding disturbances and systematic muscular changes, not only contributing to our understanding on how cats may walk, but also helping develop better legged robots.

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Open-loop control strategies for legged robot locomotion have been investigated by many researchers because of the advantages in terms of simplicity and robustness, although the influence of control inputs to locomotion performance is not fully clarified. This paper investigates two of the most basic forms of control input, sinusoidal and pulsed signals, to be used in a class of hopping robot based on parallel elastic actuation. Our results show that a pulsed torque outperforms its sinusoidal counterpart with a lower energy expenditure. Moreover, the pulsed driving torque is capable of keeping the same energy efficiency while changing the forward hopping velocity, which is not possible with the sinusoidal driving torque. Such findings will help shape the future of robotics by achieving higher energy efficiencies within legged robots, while maintaining behavioural diversity.