Intelligent RF-Based Gesture Input Devices Implemented Using e-Textiles.

We present an radio-frequency (RF)-based approach to gesture detection and recognition, using e-textile versions of common transmission lines used in microwave circuits. This approach allows for easy fabrication of input swatches that can detect a continuum of finger positions and similarly basic gestures, using a single measurement line. We demonstrate that the swatches can perform gesture detection when under thin layers of cloth or when weatherproofed, providing a high level of versatility not present with other types of approaches. Additionally, using small convolutional neural networks, low-level gestures can be identified with a high level of accuracy using a small, inexpensive microcontroller, allowing for an intelligent fabric that reports only gestures of interest, rather than a simple sensor requiring constant surveillance from an external computing device. The resulting e-textile smart composite has applications in controlling wearable devices by providing a simple, eyes-free mechanism to input simple gestures.

Miniaturized Circuitry for Capacitive Self-Sensing and Closed-Loop Control of Soft Electrostatic Transducers.

Soft robotics is a field of robotic system design characterized by materials and structures that exhibit large-scale deformation, high compliance, and rich multifunctionality. The incorporation of soft and deformable structures endows soft robotic systems with the compliance and resiliency that makes them well adapted for unstructured and dynamic environments. Although actuation mechanisms for soft robots vary widely, soft electrostatic transducers such as dielectric elastomer actuators (DEAs) and hydraulically amplified self-healing electrostatic (HASEL) actuators have demonstrated promise due to their muscle-like performance and capacitive self-sensing capabilities. Despite previous efforts to implement self-sensing in electrostatic transducers by overlaying sinusoidal low-voltage signals, these designs still require sensing high-voltage signals, requiring bulky components that prevent integration with miniature untethered soft robots. We present a circuit design that eliminates the need for any high-voltage sensing components, thereby facilitating the design of simple low cost circuits using off-the-shelf components. Using this circuit, we perform simultaneous sensing and actuation for a range of electrostatic transducers including circular DEAs and HASEL actuators and demonstrate accurate estimated displacements with errors <4%. We further develop this circuit into a compact and portable system that couples high voltage actuation, sensing, and computation as a prototype toward untethered multifunctional soft robotic systems. Finally, we demonstrate the capabilities of our self-sensing design through feedback control of a robotic arm powered by Peano-HASEL actuators.

Shape-Changing Materials Using Variable Stiffness and Distributed Control.

We describe a robotic material that tightly integrates sensing, actuation, computation, and communication to perform autonomous shape change. The composite consists of multiple cells, each with the ability to control their local stiffness (by Joule heating of a thermoplastic) and communicate with their local neighbors. We also present a distributed algorithm for calculating the inverse kinematic solution of the resulting N-body system by iteratively solving a series of problems with reduced kinematics. We describe material design choices, mechanism design, algorithm, and manufacturing, emphasizing the interdisciplinary codesign problem that robotic materials pose, and demonstrate the results from a series of shape-changing experiments.

Will robots be bodies with brains or brains with bodies?

Biology motivates how complex functionality results from systems of simple materials.

Consensus or Deadlock? Consequences of Simple Behavioral Rules for Coordination in Group Decisions.

Coordinated collective behaviors often emerge from simple rules governing the interactions of individuals in groups. We model mechanisms of coordination among ants during cooperative transport, a challenging task that requires a consensus on travel direction. Our goal is to determine whether groups following simple behavioral rules can reach a consensus using minimal information. Using deterministic and stochastic models, we investigate behavioral factors that affect coordination. We define and investigate three types of behavioral rules governing individual behavior that differ in the information available: individuals either 1) have no information, 2) can measure transport success, or 3) measure success while also knowing whether they are aligned with the majority. We find that groups break deadlocks only if individuals more readily give up when they are going against the majority, corresponding to rule type 3 -such groups are "informed." These behavioral rules succeed through positive and negative feedbacks that are implemented in our model via a single mechanism: individuals only need to measure the relative group sizes to make effective decisions. We also find that groups reach consensus more quickly if they have either a shared bias, high sensitivity to group behavior, or finely tuned persistence. Each of these is a potential adaptation for efficient cooperative transport. This flexibility makes the behavioral rules in the informed case relatively robust to deficiencies in the individuals' capabilities. While inspired by ants, our results are generalizable to other collective decisions with deadlocks, and demonstrate that groups of behaviorally simple individuals with no memory and extremely limited information can break symmetry and reach a consensus in a decision between two equal options.

Texture recognition and localization in amorphous robotic skin.

We present a soft robotic skin that can recognize and localize texture using a distributed set of sensors and computational elements that are inspired by the Pacinian corpuscle, the fast adapting, uniformly spaced mechanoreceptor with a wide receptive field, which is responsive to vibrations due to rubbing or slip on the skin. Tactile sensing and texture recognition is important for controlled manipulation of objects and navigating in one's environment. Yet, providing robotic systems or prosthetic devices with such capability at high density and bandwidth remains challenging. Each sensor node in the presented skin is created by collocating computational elements with individual microphones. These nodes are networked in a lattice and embedded in EcoFlex rubber, forming an amorphous medium. Unlike existing skins consisting of passive sensor arrays that feed into a central computer, our approach allows for detecting, conditioning and processing of tactile signals in-skin, facilitating the use of high-bandwidth signals, such as vibration, and allowing nodes to respond only to signals of interest. Communication between nodes allows the skin to localize the source of a stimulus, such as rubbing or slip, in a decentralized manner. Signal processing by individual nodes allows the skin to estimate the material touched. Combining these two capabilities, the skin is able to convert high-bandwidth, spatiotemporal information into low-bandwidth, event-driven information. Unlike taxel-based sensing arrays, this amorphous approach greatly reduces the computational load on the central robotic system. We describe the design, analysis, construction, instrumentation and programming of the robotic skin. We demonstrate that a 2.8 square meter skin with 10 sensing nodes is capable of localizing stimulus to within 2 centimeters, and that an individual sensing node can identify 15 textures with an accuracy of 71%. Finally, we discuss how such a skin could be used for full-body sensing in existing robots, augment existing sensing modalities, and how this material may be useful in robotic grasping tasks.