Unconventional computing devices are increasingly of interest as they can operate in environments hostile to silicon-based electronics, or compute in ways that traditional electronics cannot. Mechanical computers, wherein information processing is a material property emerging from the interaction of components with the environment, are one such class of devices. This information processing can be manifested in various physical substrates, one of which is granular matter. In a granular assembly, vibration can be treated as the information-bearing mode. This can be exploited to realize "polycomputing": materials can be evolved such that a single grain within them can report the result of multiple logical operations simultaneously at different frequencies, without recourse to quantum effects. Here, we demonstrate the evolution of a material in which one grain acts simultaneously as two different NAND gates at two different frequencies. NAND gates are of interest as any logical operations can be built from them. Moreover, they are nonlinear thus demonstrating a step toward general-purpose, computationally dense mechanical computers. Polycomputation was found to be distributed across each evolved material, suggesting the material's robustness. With recent advances in material sciences, hardware realization of these materials may eventually provide devices that challenge the computational density of traditional computers.

A vital component of intelligent action is affordance detection: understanding what actions external objects afford the viewer. This requires the agent to understand the physical nature of the object being viewed, its own physical nature, and the potential relationships possible when they interact. Although robotics researchers have investigated affordance detection, the way in which the morphology of the robot facilitates, obstructs, or otherwise influences the robot's ability to detect affordances has yet to be studied. We do so here and find that a robot with an appropriate morphology can evolve to predict whether it will fit through an aperture with just minimal tactile feedback. We also find that some robot morphologies facilitate the evolution of more accurate affordance detection, while others do not if all have the same evolutionary optimization budget. This work demonstrates that sensation, thought, and action are necessary but not sufficient for understanding how affordance detection may evolve in organisms or robots: morphology must also be taken into account. It also suggests that, in the future, we may optimize morphology along with control in order to facilitate affordance detection in robots, and thus improve their reliable and safe action in the world.

Continuous enhancement in wearable technologies has led to several innovations in the healthcare, virtual reality, and robotics sectors. One form of wearable technology is wear-able sensors for kinematic measurements of human motion. However, measuring the kinematics of human movement is a challenging problem as wearable sensors need to conform to complex curvatures and deform without limiting the user's natural range of motion. In fine motor activities, such challenges are further exacerbated by the dense packing of several joints, coupled joint motions, and relatively small deformations. This work presents the design, fabrication, and characterization of a thin, breathable sensing glove capable of reconstructing fine motor kinematics. The fabric glove features capacitive sensors made from layers of conductive and dielectric fabrics, culminating in a non-bulky and discrete glove design. This study demonstrates that the glove can reconstruct the joint angles of the wearer with a root mean square error of 7.2 degrees, indicating promising applicability to dynamic pose reconstruction for wearable technology and robot teleoperation.

Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and significant deformations, which enable them to navigate unstructured terrains and survive harsh impacts. They are hard to control, however, due to high dimensionality, complex dynamics, and a coupled architecture. Physics-based simulation is a promising avenue for developing locomotion policies that can be transferred to real robots. Nevertheless, modeling tensegrity robots is a complex task due to a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real (R2S2R) strategy for tensegrity robots. This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot. These data include offline measurements of physical properties, such as mass and geometry for various robot components, and the observation of a trajectory using a random control policy. With the data from the real robot, the engine can be iteratively refined and used to discover locomotion policies that are directly transferable to the real robot. Beyond the R2S2R pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function for matching tensegrity locomotion gaits, and a trajectory segmentation technique that avoids conflicts in gradient evaluation during training. Multiple iterations of the R2S2R process are demonstrated and evaluated on a real 3-bar tensegrity robot.

Digital signal processors are widely used in today's computers to perform advanced computational tasks. But, the selection of digital electronics as the physical substrate for computation a hundred years ago was influenced more by technological limitations than substrate appropriateness. In recent decades, advances in chemical, physical and material sciences have provided new options. Granular metamaterials are one such promising target for realizing mechanical computing devices. However, their high-dimensional design space and the unintuitive relationship between microstructure and desired macroscale behavior makes the inverse design problem formidable. In this paper, we use multiobjective evolutionary optimization to solve this inverse problem: we demonstrate the design of basic logic gates embedded in a granular metamaterial, and that the designed material can be "reprogrammed" via frequency modulation. As metamaterial design advances, more computationally dense materials may be evolved, amenable to reprogramming by increasingly sophisticated programming languages written in the frequency domain.

Compliant robotic grippers are more robust to uncertainties in grasping and manipulation tasks, especially when paired with tactile and proprioceptive feedback. Although considerable progress has been made towards achieving proprioceptive soft robotic grippers, current efforts require complex driving hardware or fabrication techniques. In this paper, we present a simple scalable soft robotic gripper integrated with high-deformation strain and pressure sensors. The gripper is composed of structurally-compliant handed shearing auxetic structures actuated by electric motors. Coupling deformable sensors with the compliant grippers enables gripper proprioception and object classification. With this sensorized system, we are able to identify objects' size to within 33% of actual radius and sort objects as hard/soft with 78% accuracy.

In this paper, we present sensor-controlled antagonistic pneumatic actuators (SCAPAs) that integrate proven soft robotic actuators and sensors into a simplified, controllable design. The antagonistic actuators together compose a bidirectional bending actuator with embedded capacitive strain sensors. By designing the SCAPAs from the ground-up for closed-loop control, we are able to minimize both the number of constituent components and the types of materials used, and further streamline the manufacturing processes. These improvements are embodied in the multipurpose use of a single conductive fabric sheet for both actuation and sensing, integrated into an otherwise all-silicone device. Such reduced material complexity allows us to use simple finite element analysis (FEA) models to predict the performance of a given design. We compare various designs to maximize sensor effectiveness using FEA and experimentally verify the suitability of select designs for state reconstruction. After converging on our final design, we demonstrate that this design evaluation process enables the use of simple control strategies to achieve closed-loop control.

In this paper, we describe the fabrication and testing of a stretchable fabric sleeve with embedded elastic strain sensors for state reconstruction of a soft robotic joint. The strain sensors are capacitive and composed of graphite-based conductive composite electrodes and a silicone elastomer dielectric. The sensors are screenprinted directly into the fabric sleeve, which contrasts the approach of pre-fabricating sensors and subsequently attaching them to a host. We demonstrate the capabilities of the sensor-embedded fabric sleeve by determining the joint angle and end effector position of a soft pneumatic joint with similar accuracy to a traditional IMU. Furthermore, we show that the sensory sleeve is capable of capturing more complex material states, such as fabric buckling and non-constant curvatures along linkages and joints.

Materials exhibiting variable stiffness properties have great potential for use in the growing field of soft robotics. Soft structural materials allow a robot to fit into enclosed spaces, resist shock and vibration, or even reconfigure its geometry and adapt to various environments. Rigid structural materials on the other hand allow environmental interactions through application of force and load-bearing capabilities. Materials that can be selectively switched between these two extremes could greatly expand the functionality of a robot that requires the properties of both. In this paper, we introduce a conductive epoxy composite that is self-softening through Joule heating via direct application of electrical current. The polymer can then become load-bearing and rigid again after being formed into a new shape. We demonstrate the capabilities of this material by attaching a pneumatic actuator and showing that the resulting variable stiffness device can be softened from its initial rigid state, reshape itself using the pneumatic actuator, then become rigid again and hold this new position without additional power being supplied to the actuator.

In this paper, we present a fluidically functionalized soft-bodied robot that integrates both sensing and actuation. Rather than combining these functions as an afterthought, we design sensors and actuators into the robot at the onset, both reducing fabrication complexity and optimizing component interactions. We utilize liquid metal strain sensors and pneumatic actuators embedded into a silicone robotic gripper. The robot's body is formed by curing the silicone in complex 3D printed molds. We show that the liquid metal strain gauges provide a repeatable resistance response during robotic actuation. We further show that, given sufficient control over other time-dependent variables, it is possible to determine when the robot begins gripping an object during actuation.

Materials with variable stiffness have the potential to provide a range of new functionalities, including system reconfiguration by tuning the location of rigid links and joints. In particular, wearable applications would benefit from variable stiffness materials in the context of active braces that may stiffen when necessary and soften when mobility is required. In this work, we present fibers capable of adjusting to provide variable stiffness in wearable fabrics. The variable stiffness fibers are made from shape memory materials, where shape memory alloy (SMA) is coated with a thin film of shape memory polymer (SMP). The fibers, which are fabricated via a continuous feed-through process, reduce in bending stiffness by an order of magnitude when the SMP goes through the glass transition. The transition between rubbery and glassy state is accomplished by direct joule heating of the embedded SMA wire. We employ a COMSOL model to relate the current input to the time required for the fibers to transition between stiffness states. Finally, we demonstrate how this device can be worn and act as a joint stability brace on human fingers.

Future generations of wearable robots will include systems constructed from conformable materials that do not constrain the natural motions of the wearer. Fabrics represent a class of highly conformable materials that have the potential for embedded function and are highly integrated into our daily lives. In this work, we present a robotic fabric with embedded actuation and sensing. Attaching the same robotic fabric to a soft body in different ways leads to unique motions and sensor modalities with many different applications for robotics. In one mode, the robotic fabric acts around the circumference of the body, and compression of the body is achieved. Attaching the robotic fabric in another way, along one surface of a body for example, bending is achieved. We use thread-like actuators and sensors to functionalize fabric via a standard textile manufacturing process (sewing). The actuated fabric presented herein yields a contractile force of 9.6N and changes in length by approximately 60% when unconstrained. The integrated strain sensor is evaluated and found to have an RMS error of 14.6%, and qualitatively differentiates between the compressive and bending motions demonstrated.

Micromanipulation methods used for complicated tasks such as microrobot assembly and microvascular surgery often lack the force reflection and contact localization capability necessary to achieve robust grasps of micro-scale objects without applying excessive forces. This absence of haptic feedback is especially prohibitive in cases where visual evidence of force application, such as object surface deformation, is imperceptible and where unstructured, dynamically changing environments require force sensing and modulation for safe, atraumatic object manipulation. This paper describes the design, fabrication, and experimental validation of a soft tactile sensor array for sub-millimeter contact localization and contact force measurement during micromanipulation. The geometry and placement of conductive liquid embedded channels within the sensor array are optimized to provide adequate sensitivity for representative micro-manipulation tasks. Mechanical testing of the sensor demonstrates a sensitivity of less than 50mN and contact localization resolution on the order of 100's of microns.

A hyperelastic, thin, transparent pressure sensitive keypad is fabricated by embedding a silicone rubber film with conductive liquid-filled microchannels. Applying pressure to the surface of the elastomer deforms the cross-section of underlying microchannels and changes the electrical resistance across the affected channels. Perpendicular conductive channels form a quasi-planar network within an elastomeric matrix that registers the location, intensity and duration of applied pressure. Pressing channel intersections of the keypad triggers one of twelve keys, allowing the user to write any combination of alphabetic letters. A 5% change in channel output voltage must be achieved to trigger a key. It is found that approximately 100 kPa of pressure is necessary to produce a 5% change in voltage across a conductive microchannel that is 20 microns in height and 200 microns in width. Sensitivity of the keypad is tunable via channel geometry and choice of elastomeric material.

We introduce a curvature sensor composed of a thin, transparent elastomer film (polydimethylsiloxane, PDMS) embedded with a microchannel of conductive liquid (eutectic Gallium Indium, eGaIn) and a sensing element. Bending the sensor exerts pressure on the embedded microchannel via the sensing element. Deformation of the cross-section of the microchannel leads to a change in electrical resistance. We demonstrate the functionality of the sensor through testing on a finger joint. The film is wrapped around a finger with the sensing element positioned on top of the knuckle. Finger bending both stretches the elastomer and exerts pressure on the sensing element, leading to an enhanced change in the electrical resistance. Because the sensor is soft (elastic modulus E ~ 1 MPa) and stretchable (>350%), it conforms to the host bending without interfering with the natural mechanics of motion. This sensor represents the first use of liquid-embedded elastomer electronics to monitor human or robotic motion.

Programmable materials based on robotic origami have been demonstrated with the capability to fold into 3D shapes starting from a nominally 2D sheet. This concept requires high torque density actuators, flexible electronics and an integrated substrate. We report on two types of stretchable circuitry that are directly applicable to robotic origami: meshed copper traces and liquid-metal-filled channels in an elastomer substrate. Both methods maintain conductivity even at large strains (during stretching) and curvatures (during folding). Both circuit designs are integrated with a tiled origami module actuated by a shape memory alloy actuator. We also integrate a soft curvature sensor into the robotic origami module that measures the full range of motion of the module in real-time.