Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at the material level. These robots use deformable materials capable of shape and behavioral changes and allow conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable of stretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges of real-world environments.

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Robots generally excel at specific tasks in structured environments but lack the versatility and the adaptability required to interact with and locomote within the natural world. To increase versatility in robot design, we present robotic skins that can wrap around arbitrary soft bodies to induce the desired motions and deformations. Robotic skins integrate actuation and sensing into a single conformable material and may be leveraged to create a multitude of controllable soft robots with different functions or gaits to accommodate the demands of different environments. We show that attaching the same robotic skin to a soft body in different ways, or to different soft bodies, leads to distinct motions. Further, we show that combining multiple robotic skins enables complex motions and functions. We demonstrate the versatility of this soft robot design approach in a wide range of applications—including manipulation tasks, locomotion, and wearables—using the same two-dimensional (2D) robotic skins reconfigured on the surface of various 3D soft, inanimate objects.

Fig. 1. The top 100 simulated 2-by-2-by-2 configurations of passive (cyan) and volumetrically-actuating (red) voxels (a) were manufactured in reality (b).

Fig. 1. After learning to walk, a simulated quadruped is subjected to unanticipated insult: its legs are cut off. An evolutionary algorithm searches for deformations to the postdamage structure that, when coupled with the predamage controller, result in function recovery. One of the evolved solutions (shown here) yields the spontaneous "regeneration" of the lost legs, which was manually transferred to reality (youtu.be/afOXX2r54mQ).Abstract-A robot's mechanical parts routinely wear out from normal functioning and can be lost to injury. For autonomous robots operating in isolated or hostile environments, repair from a human operator is often not possible. Thus, much work has sought to automate damage recovery in robots. However, every case reported in the literature to date has accepted the damaged mechanical structure as fixed, and focused on learning new ways to control it. Here we show for the first time a robot that automatically recovers from unexpected damage by deforming its resting mechanical structure without changing its control policy. We found that, especially in the case of "deep insult", such as removal of all four of the robot's legs, the damaged machine evolves shape changes that not only recover the original level of function (locomotion) as before, but can in fact surpass the original level of performance (speed). This suggests that shape change, instead of control readaptation, may be a better method to recover function after damage in some cases.

are specialized for a single task, and cannot adapt their body to accomplish additional tasks after manufacture. Moreover, biological bodies are often highly regenerative, and able to repair and reconfigure their large-scale architecture in the face of significant damage or radical changes to their components. [3] For example, salamanders regenerate amputated limbs, [4] and fragments cut from arbitrary portions of planaria flatworms can rebuild (and rescale) their bodies to recover a full, correct anatomy. [3] Remarkably, many of these systems are able to retain information, such as learned memories, despite drastic reconfiguration or total replacement of their brains. [5] In these integrated living systems, intelligence, memory, learning, behavior, and body structure are all intertwined and emerge from the multiscale dynamics of the same robust and highly fault-tolerant medium.Evolution did not result in hard-coded body plans purely determined by genetic factors, but rather produced diverse examples of intelligent self-modifying systems which adapt to numerous extragenomic influences. [6] In this way, biology serves as an important proof-of-principle, and design challenge, for artificial intelligence and shape changing robots. Despite having access to this extensive set of model systems, the realization of general-purpose, adaptive robots has remained elusive. Researchers have proposed modular robots that can be attached to each other to expand functionality, [7] passively conforming universal grippers, [8] reconfigurable robotic skins, [9] self-assembling robot swarms, [10] gait-switching mechanisms [11] and controllers, [12,13] and algorithms that quickly re-adapt to multiple distinct tasks. [14] Such approaches succeed at adaptation but operate under the assumption that the robot's body is only reconfigured or reshaped due to external forces, and do not explore the possibility of synthetic machines that actively grow, regenerate, deform, or otherwise change the resting shape of their constituent components.With the introduction of a conformable gripper by Hirose in 1978, [15] followed by continuum robot arms, [16] silicone grippers, [17] and variable stiffness actuators, [18] robots that can adapt to real-world environments by changing their shape are becoming closer to reality. In particular, the idea of passively conforming around objects during grasping has been quite successful. [17,19,20] Soft robots have shown potential in other applications, including human-robot interaction and exploration, as reviewed by Kim et al., [21] Rus et al., [22] and others. [23,24] For a comprehensive review of the role of deformation in singlefunction soft robots, the reader is referred to Wang et al. [25] One of the key differentiators between biological and artificial systems is the dynamic plasticity of living tissues, enabling adaptation to different environmental conditions, tasks, or damage by reconfiguring physical structure and behavioral control policies. Lack of dynamic plasticity is a significant limitation for a...

Compliant sensors based on composite materials are necessary components for geometrically complex systems such as wearable devices or soft robots. Composite materials consisting of polymer matrices and conductive fillers have facilitated the manufacture of compliant sensors due to their potential to be scaled in printing processes. Printing composite materials generally entails the use of solvents, such as toluene or cyclohexane, to dissolve the polymer resin and thin down the material to a printable viscosity. However, such solvents cause swelling and decomposition of most polymer substrates, limiting the utility of the composite materials. Moreover, many such conventional solvents are toxic or otherwise present health hazards. Here, sustainable manufacturing of sensors is reported, which uses an ethanol-based Pickering emulsion that spontaneously coagulates and forms a conductive composite. The Pickering emulsion consists of emulsified polymer precursors stabilized by conductive nanoparticles in an ethanol carrier. Upon evaporation of the ethanol, the precursors are released, which then coalesce amid nanoparticle networks and spontaneously polymerize in contact with the atmospheric moisture. We printed the self-coagulating conductive Pickering emulsion onto a variety of soft polymeric systems, including all-soft actuators and conventional textiles, to sensitize these systems. The resulting compliant sensors exhibit high strain sensitivity with negligible hysteresis, making them suitable for wearable and robotic applications.