Thin, soft, and elastic electronics with physical properties well matched to the epidermis can be conformally and robustly integrated with the skin. Materials and optimized designs for such devices are presented for surface electromyography (sEMG). The findings enable sEMG from wide ranging areas of the body. The measurements have quality sufficient for advanced forms of human-machine interface.

Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain-computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide highfidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steadystate visually evoked potential-based brain-computer interface and elicitation of an event-related potential (P300 wave).or more than 80 y, electroencephalography (EEG) has provided an effective noninvasive means to study human brain activity (1). EEG is instrumental in a wide range of clinical and research applications, from diagnosing epilepsy (2) to improving our understanding of language comprehension (3) and the development of brain-computer interfaces (BCI) (4). Conventional EEG recording systems, particularly the physical interface between the sensor (commonly known as an electrode) and the head, have limitations that constrain the more widespread use of EEG monitoring. Electrodes typically consist of rigid metal disks mechanically secured to the head with a mesh cap and chin strap, where electrolyte gels (5) enable efficient electrical coupling by reducing the impedance at the skin interface. This arrangement causes skin irritation (erythema) and leads to electrical degradation for periods of use that extend more than a few hours, typically caused by drying of the electrolyte gel (6). Recent technologies replace the gel (7, 8) with needles (8, 9), contact probes (10, 11), capacitive disks (12, 13), conductive composites (14, 15), or nanowires (16). Such dry electrodes have some promise, but they require multistep preparations, obtrusive wiring interfaces, and/or cumbersome mechanical fixtures. These shortcomings limit the potential for long-term use in diagnosis of neurological disabilities (17, 18) or in persistent BCI (17,19). For example, although microneedle electrodes can record EEG signals for a few hours (20), the interface does not offer the robustness, comfort, or eas...

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Researchers report advances in materials and designs for skin-integrated devices capable of measuring acoustic signatures.

Tracking 6-D poses of objects from videos provides rich information to a robot in performing different tasks such as manipulation and navigation. In this article, we formulate the 6-D object pose tracking problem in the Rao-Blackwellized particle filtering framework, where the 3-D rotation and the 3-D translation of an object are decoupled. This factorization allows our approach, called PoseRBPF, to efficiently estimate the 3-D translation of an object along with the full distribution over the 3-D rotation. This is achieved by discretizing the rotation space in a fine-grained manner and training an autoencoder network to construct a codebook of feature embeddings for the discretized rotations. As a result, PoseRBPF can track objects with arbitrary symmetries while still maintaining adequate posterior distributions. Our approach achieves state-of-the-art results on two 6-D pose estimation benchmarks. We open-source our implementation at https://github.com/NVlabs/PoseRBPF.

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In this paper we study the quasi-static motion of large legged robots that have many degrees of freedom. While gaited walking may suffice on easy ground, rough and steep terrain requires unique sequences of footsteps and postural adjustments specifically adapted to the terrain's local geometric and physical properties. In this paper we present a planner that computes these motions by combining graph searching to generate a sequence of candidate footfalls with probabilistic sample-based planning to generate continuous motions that reach these footfalls. To improve motion quality, the probabilistic planner derives its sampling strategy from a small set of motion primitives that have been generated offline. The viability of this approach is demonstrated in simulation for the six-legged Lunar vehicle ATHLETE and the humanoid HRP-2 on several example terrains, including one that requires both hand and foot contacts and another that requires rappelling.