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In this paper a model is presented which describes an octopus-inspired robot capable of two kinds of locomotion: crawling and bipedal walking. Focus will be placed on the latter type of locomotion to demonstrate, through model simulations and experimental trials, that the robot’s speed increases by about 3 times compared to crawling. This finding is coherent with the performances of the biological counterpart when adopting this gait. Specific features of underwater legged locomotion are then derived from the model, which prompt the possibility of controlling locomotion by using simple control and by exploiting slight morphological adaptations.

Recent developments in robotics demonstrated that bioinspiration and embodiement are powerful tools to achieve robust behavior in presence of little control. In this context morphological design is usually performed by humans, following a set of heuristic principles: in general this can be limiting, both from an engineering and an artificial life perspectives. In this work we thus suggest a different approach, leveraging evolutionary techniques. The case study is the one of improving the locomotion capabilities of an existing bioinspired robot. First, we explore the behavior space of the robot to discover a number of qualitatively different morphology-enabled behaviors, from whose analysis design indications are gained. The suitability of novelty search -- a recent open-ended evolutionary algorithm -- for this intended purpose is demonstrated. Second, we show how it is possible to condense such behaviors into a reconfigurable robot capable of online morphological adaptation (morphosis, morphing). Examples of successful morphing are demonstrated, in which changing just one morphological parameter entails a dramatic change in the behavior: this is promising for a future robot design. The approach here adopted represents a novel computed-aided, bioinspired, design paradigm, merging human and artificial creativity. This may result in interesting implications also for artificial life, having the potential to contribute in exploring underwater locomotion "as-it-could-be".

In recent years a number of robotic platforms have been developed, that are capable of robust locomotion in presence of a simple open loop control. Relying on the self-stabilizing properties of their mechanical structure, morphology assumes a crucial role in the design process, that is, however, usually guided by a set of heuristic principles falling under what is commonly known as embodied intelligence. Despite many impressive demonstrations, the result of such a methodology may be sub-optimal, given the dimension of the design space and the complex intertwining of involved dynamical effects. Encouraged by the growing consensus that embodied solutions can indeed be produced by bio-inspired computational techniques in a more automated manner, this work proposes a computer-aided methodology to explore in simulation the design space of an existing robot, by harnessing computational techniques inspired by natural evolution. Although many works exist on the application of evolutionary algorithms in robotics, few of them embrace this design perspective. The idea is to have an evolutionary process suggesting to the human designer a number of interesting robot configurations and embodied behaviors, from whose analysis design hints can be gained to improve the platform. The focus will be on enhancing the locomotion capabilities of a multi-legged, soft, underwater robot. We investigate for the first time the suitability of a recently introduced open-ended evolutionary algorithm (novelty search) for the intended study, and demonstrate its benefits in the comparison with a more conventional genetic algorithm. Results confirm that evolutionary algorithms are indeed capable of producing new, elaborate dynamic gaits, with evolved designs exhibiting several regularities. Possible future directions are also pointed out, in which the passive exploitation of robot's morphological features could bring additional advantages in achieving diverse, robust behaviors.

This paper reports the design process, the implementation, and the results of a novel robotic contest addressing soft robots, named RoboSoft Grand Challenge. Application-oriented tasks were proposed in three different scenarios where soft robotics is particularly lively: manipulation, terrestrial, and underwater locomotion. Starting from about 60 expressions of interest submitted by international teams distributed across the world, 19 robots were eventually selected to participate in the challenge in two of the initially proposed scenarios, i.e., manipulation and terrestrial locomotion. Results highlight both the effectiveness and limitations of state of the art soft robots with respect to the selected tasks. The paper will also focus on some of the advantages and disadvantages of contests as technology-steering mechanisms, including what we called “reductionist design,” a phenomenon in which simplistic solutions are devised to purposely tackle the proposed tasks, possibly hindering more general and desired technological advancements.