In both biological and engineered systems, functioning at peak power output for prolonged periods of time requires thermoregulation. Here, we report a soft hydrogel-based actuator that can maintain stable body temperatures via autonomic perspiration. Using multimaterial stereolithography, we three-dimensionally print finger-like fluidic elastomer actuators having a poly-N-isopropylacrylamide (PNIPAm) body capped with a microporous (~200 micrometers) polyacrylamide (PAAm) dorsal layer. The chemomechanical response of these hydrogel materials is such that, at low temperatures (30°C), the pores dilate to enable localized perspiration in the hydraulic actuator. Such sweating actuators exhibit a 600% enhancement in cooling rate (i.e., 39.1°C minute⁻¹) over similar non-sweating devices. Combining multiple finger actuators into a single device yields soft robotic grippers capable of both mechanically and thermally manipulating various heated objects. The measured thermoregulatory performance of these sweating actuators (~107 watts kilogram⁻¹) greatly exceeds the evaporative cooling capacity found in the best animal systems (~35 watts kilogram⁻¹) at the cost of a temporary decrease in actuation efficiency.

A soft optical sensor network distributed within a soft body measures the location and degree of deformation.

Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches.

In this paper we describe the conceptual design and implementation of the SIMBA manipulator, designed and developed for participating in the RoboSoft Grand Challenge 2016. In our novel design we have proposed: 1) a modular continuum arm with independent actuation units for each module, to increase maintainability; 2) a soft reconfigurable hand, for a better adaptation of the fingers to objects of different shapes and size; 3) a moving base for increasing the workspace. We used a hybrid approach in designing and manufacturing by integrating soft and hard components, in both materials and actuation, providing high lateral stiffness in the arm through flat springs, soft joints in fingers for more compliancy and tendon-motor actuation mechanism providing flexibility but at the same time precision and speed. The SIMBA manipulator has demonstrated excellent grasping and manipulation capabilities by being able to grasp objects with different fragility, geometry and size; and by lifting objects with up to 2Kg of weight it demonstrate also to be robust and reliable. The experimental results pointed out that our design and approach can lead to the realization of robots able to act in unknown and unstructured environments in synergy with humans, for a variety of applications where compliancy is fundamental, preserving robustness and safety.

This work addresses the challenge of underactuated pattern generation in continuous multistable structures. The examined configuration is a slender membrane which can concurrently sustain two different equilibria states, separated by transition regions, and is actuated by a viscous fluid. We first demonstrate the formation and motion of a single transition region and then sequencing of several such moving transition regions to achieve arbitrary patterns by controlling the inlet pressure of the actuating fluid. Finally, we show that nonuniform membrane properties, along with transient dynamics of the fluid, can be leveraged to directly snap through any segment of the membrane.

We present the basic module of a modular continuum arm (soft compliant manipulator for broad applications (SIMBA)). SIMBA is a robotic arm with a hybrid structure, namely a combination of rigid and soft components, which makes the arm highly versatile, dexterous, and robust. These key features are due to the design of its basic module, which is characterized by a three-dimensional workspace with a constant radius around its rotation axis, large and highly repeatable bending, complete rotation, and passive stiffness. We present an extensive analysis and characterization of the basic module of the SIMBA arm in terms of design, fabrication, kinematic model, stiffness, and bending behavior. All the theoretical models presented were validated with empirical results. Our findings show a positional typical error of less than ≈6% in module diameter (highly repeatable) with a passive stiffness of 0.8 N/mm (≈1 kg load). Our aim is to demonstrate that this kind of robotic element can be exploited as an elementary module of a more complex structure, which can be used in any application requiring high directional stiffness but without the need for an active stiffness mechanism, as is the case in daily activities (e.g., door opening, water pouring, obstacle avoidance, and manipulation tasks).