Soft actuators are the components responsible for producing motion in soft robots. Although soft actuators have allowed for a variety of innovative applications, there is a need for design tools that can help to efficiently and systematically design actuators for particular functions. Mathematical modeling of soft actuators is an area that is still in its infancy but has the potential to provide quantitative insights into the response of the actuators. These insights can be used to guide actuator design, thus accelerating the design process. Here, we study fluid-powered fiberreinforced actuators, because these have previously been shown to be capable of producing a wide range of motions. We present a design strategy that takes a kinematic trajectory as its input and uses analytical modeling based on nonlinear elasticity and optimization to identify the optimal design parameters for an actuator that will follow this trajectory upon pressurization. We experimentally verify our modeling approach, and finally we demonstrate how the strategy works, by designing actuators that replicate the motion of the index finger and thumb.soft robotics | fiber-reinforced actuators | customized actuators I n the field of robotics, it is essential to understand how to design a robot such that it can perform a particular motion for a target application. For example, this robot could be a robot arm that moves along a certain path or a wearable robot that assists with motion of a limb. For conventional hard robots, methods have been developed to describe the forward kinematics (i.e., for given actuator inputs, what will the configuration of the robot be) and inverse kinematics (i.e., for a desired configuration of the robot, what should the actuator inputs be) (1-4).Recently, there has been significant progress in the field of soft robotics, with the development of many soft grippers (5, 6), locomotion robots (7,8), and assistive devices (9). Although their inherent compliance, easy fabrication, and ability to achieve complex output motions from simple inputs have made soft robots very popular (10, 11), there is growing recognition that the development of methods for efficiently designing actuators for particular functions is essential to the advancement of the field. To this end, some research groups have begun focusing their efforts on modeling and characterizing soft actuators (12-20). In particular, significant progress has been made on solving the forward kinematics problem (16-19) and even on using dynamic modeling to perform motion planning (14). However, the practical problem of designing a soft actuator to achieve a particular motion remains an issue. Finite element (FE) analysis has previously been used as a design tool to find the optimal geometric parameters for a soft pneumatic actuator, given some design criteria (15). Although this procedure yields some nice results, only basic motions (linear or bending) were studied, because the method is computationally intensive. An alternative approach is to use analytical modeling...

In this work we investigate the influence of fiber angle on the deformation of fiber-reinforced soft fluidic actuators. We demonstrate that, by simply varying the fiber angle, we can tune the actuators to achieve a wide range of motions, including axial extension, radial expansion, and twisting. We investigate the relationship between fiber angle and actuator deformation by performing finite element simulations for actuators with a range of different fiber angles, and we verify the simulation results by experimentally characterizing the actuators. By combining actuator segments in series, we can achieve combinations of motions tailored to specific tasks. We demonstrate this by using the results of simulations of separate actuators to design a segmented wormlike soft robot capable of propelling itself through a tube and performing an orientation-specific peg insertion task at the end of the tube. Understanding the relationship between fiber angle and motion of these soft fluidic actuators enables rapid exploration of the design space, opening the door to the iteration of exciting soft robot concepts such as flexible and compliant endoscopes, pipe inspection devices, and assembly line robots.

Within decisions, perceived alternatives compete until one is preferred. Across decisions, the playing field on which these alternatives compete evolves to favor certain alternatives. Mouse cursor trajectories provide rich continuous information related to such cognitive processes during decision making. In three experiments, participants learned to choose symbols to earn points in a discrimination learning paradigm and the cursor trajectories of their responses were recorded. Decisions between two choices that earned equally high-point rewards exhibited far less competition than decisions between choices that earned equally low-point rewards. Using positional coordinates in the trajectories, it was possible to infer a potential field in which the choice locations occupied areas of minimal potential. These decision spaces evolved through the experiments, as participants learned which options to choose. This visualisation approach provides a potential framework for the analysis of local dynamics in decision-making that could help mitigate both theoretical disputes and disparate empirical results.

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