Maneuverable gait selection for a novel fish-inspired robot using a CMA-ES-assisted workflow.

Among underwater vehicles, fish-inspired designs are often selected for their efficient gaits; these designs, however, remain limited in their maneuverability, especially in confined spaces. This paper presents a new design for a fish-inspired robot with two degree-of-freedom pectoral fins and a single degree-of-freedom caudal fin. This robot has been designed to operate in open-channel canals in the presence of external disturbances. With the complex interactions of water in mind, the composition of goal-specific swimming gaits is trained via a machine learning workflow in which automated trials in the lab are used to select a subset of potential gaits for outdoor trials. The goal of this process is to minimize the time cost of outdoor experimentation through the identification and transfer of high-performing gaits with the understanding that, in the absence of complete replication of the intended target environment, some or many of these gaits must be eliminated in the real world. This process is motivated by the challenge of balancing the optimization of complex, high degree-of-freedom robots for disturbance-heavy, random, niche environments against the limitations of current machine learning techniques in real-world experiments, and has been used in the design process as well as across a number of locomotion goals. The key contribution of this paper involves finding strategies that leverage online learning methods to train a bio-inspired fish robot by identifying high-performing gaits that have a consistent performance both in the laboratory experiments and the intended operating environment. Using the workflow described herein, the resulting robot can reach a forward swimming speed of 0.385 m s-1(0.71 body lengths per second) and can achieve a near-zero turning radius.

Tunable Sponge-Like Hierarchically Porous Hydrogels with Simultaneously Enhanced Diffusivity and Mechanical Properties.

Crosslinked polymers and gels are important in soft robotics, solar vapor generation, energy storage, drug delivery, catalysis, and biosensing. However, their attractive mass transport and volume-changing abilities are diffusion-limited, requiring miniaturization to avoid slow response. Typical approaches to improving diffusion in hydrogels sacrifice mechanical properties by increasing porosity or limit the total volumetric flux by directionally confining the pores. Despite tremendous efforts, simultaneous enhancement of diffusion and mechanical properties remains a long-standing challenge hindering broader practical applications of hydrogels. In this work, cononsolvency photopolymerization is developed as a universal approach to overcome this swelling-mechanical property trade-off. The as-synthesized poly(N-isopropylacrylamide) hydrogel, as an exemplary system, presents a unique open porous network with continuous microchannels, leading to record-high volumetric (de)swelling speeds, almost an order of magnitude higher than reported previously. This swelling enhancement comes with a simultaneous improvement in Young's modulus and toughness over conventional hydrogels fabricated in pure solvents. The resulting fast mass transport enables in-air operation of the hydrogel via rapid water replenishment and ultrafast actuation. The method is compatible with 3D printing. The generalizability is demonstrated by extending the technique to poly(N-tertbutylacrylamide-co-polyacrylamide) and polyacrylamide hydrogels, non-temperature-responsive polymer systems, validating the present hypothesis that cononsolvency is a generic phenomenon driven by competitive adsorption.

Heterogeneous Hydrogel Structures with Spatiotemporal Reconfigurability using Addressable and Tunable Voxels.

Stimuli-responsive hydrogels can sense environmental cues and change their volume accordingly without the need for additional sensors or actuators. This enables a significant reduction in the size and complexity of resulting devices. However, since the responsive volume change of hydrogels is typically uniform, their robotic applications requiring localized and time-varying deformations have been challenging to realize. Here, using addressable and tunable hydrogel building blocks-referred to as soft voxel actuators (SVAs)-heterogeneous hydrogel structures with programmable spatiotemporal deformations are presented. SVAs are produced using a mixed-solvent photopolymerization method, utilizing a fast reaction speed and the cononsolvency property of poly(N-isopropylacrylamide) (PNIPAAm) to produce highly interconnected hydrogel pore structures, resulting in tunable swelling ratio, swelling rate, and Young's modulus in a simple, one-step casting process that is compatible with mass production of SVA units. By designing the location and swelling properties of each voxel and by activating embedded Joule heaters in the voxels, spatiotemporal deformations are achieved, which enables heterogeneous hydrogel structures to manipulate objects, avoid obstacles, generate traveling waves, and morph to different shapes. Together, these innovations pave the way toward tunable, untethered, and high-degree-of-freedom hydrogel robots that can adapt and respond to changing conditions in unstructured environments.