In warehouse environments, robots require robust picking capabilities to manage a wide variety of objects. Effective deployment demands minimal hardware, strong generalization to new products, and resilience in diverse settings. Current methods often rely on depth sensors for structural information, which suffer from high costs, complex setups, and technical limitations. Inspired by recent advancements in computer vision, we propose an innovative approach that leverages foundation models to enhance suction grasping using only RGB images. Trained solely on a synthetic dataset, our method generalizes its grasp prediction capabilities to real-world robots and a diverse range of novel objects not included in the training set. Our network achieves an 81.9% success rate in real-world applications. The project website with code and data will be available at http://optigrasp.github.io.

Warehouse robotic systems equipped with vacuum grippers must reliably grasp a diverse range of objects from densely packed shelves. However, these environments present significant challenges, including occlusions, diverse object orientations, stacked and obstructed items, and surfaces that are difficult to suction. We introduce TetraGrip, a novel vacuum-based grasping strategy featuring four suction cups mounted on linear actuators. Each actuator is equipped with an optical time-of-flight (ToF) proximity sensor, enabling reactive grasping.We evaluate TetraGrip in a warehouse-style setting, demonstrating its ability to manipulate objects in stacked and obstructed configurations. Our results show that our RL-based policy improves picking success in stacked-object scenarios by 22.9% compared to a single-suction gripper. Additionally, we demonstrate that TetraGrip can successfully grasp objects in scenarios where a single-suction gripper fails due to physical limitations, specifically in two cases: (1) picking an object occluded by another object and (2) retrieving an object in a complex scenario. These findings highlight the advantages of multi-actuated, suction-based grasping in unstructured warehouse environments. The project website is available at: https://tetragrip.github.io/.

In many robotic systems, the holding state consumes power, limits operating time, and increases operating costs. Electrostatic clutches have the potential to improve robotic performance by generating holding torques with low power consumption. A key limitation of electrostatic clutches has been their low specific shear stresses which restrict generated holding torque, limiting many applications. Here we show how combining the Johnsen-Rahbek (JR) effect with the exponential tension scaling capstan effect can produce clutches with the highest specific shear stress in the literature. Our system generated 31.3 N/cm2sheer stress and a total holding torque of 7.1 N•m while consuming only 2.5 mW/cm2at 500 V. We demonstrate a theoretical model of an electrostatic adhesive capstan clutch and demonstrate how large angle (θ > 2π) designs increase efficiency over planar or small angle (θ < π) clutch designs. We also report the first unfilled polymeric material, polybenzimidazole (PBI), to exhibit the JR-effect.

New methods of passive wireless communication are presented where no RF carrier is needed. Instead, data is wirelessly transmitted by modulating noise sources, from those found in electronic components to extraterrestrial noise sources. Any pair of noise sources with a difference in noise temperature can be used to enable communication. We discuss using the Earth, the Moon, the Sun, the coldness of space, and Active Cold Load circuits as sources of thermal contrast. We present Cosmic Backscatter and demonstrate that wireless connectivity can be enabled by switching an antenna connection between the "cold" Sky and a "hot" 50Ω resistor. Furthermore, we present Noise Suppression Communication, where data is transmitted by controlling an Active Cold Load to selectively reduce emitted noise below ambient temperature levels.

Autocurricular training is an important sub-area of multi-agent reinforcement learning (MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving scheme. The robotics community has experimented auto-curricular training with physically grounded problems, such as robust control and interactive manipulation tasks. However, the asymmetric nature of these tasks makes the generation of sophisticated policies challenging. Indeed, the asymmetry in the environment may implicitly or explicitly provide an advantage to a subset of agents which could, in turn, lead to a low-quality equilibrium. This paper proposes a novel game-theoretic algorithm, Stackelberg Multi-Agent Deep Deterministic Policy Gradient (ST-MADDPG), which formulates a two-player MARL problem as a Stackelberg game with one player as the ‘leader’ and the other as the ‘follower’ in a hierarchical interaction structure wherein the leader has an advantage. We first demonstrate that the leader's advantage from ST-MADDPG can be used to alleviate the inherent asymmetry in the environment. By exploiting the leader's advantage, ST-MADDPG improves the quality of a co-evolution process and results in more sophisticated and complex strategies that work well even against an unseen strong opponent.

We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low power (1.1 mW) but only outputs grey-scale, low resolution and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data for this camera system is then wirelessly sent to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher-resolution color video. To achieve this, we introduce an attention feature filter mechanism that assigns different weights to different features, based on the correlation between the feature map and the contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluations show that our dual-camera approach reduces energy consumption by 7x compared to existing systems. Further, our model achieves an average greyscale PSNR gain of 3.7 dB over prior single and dual-camera video super-resolution methods and 5.6 dB RGB gain over prior color propagation methods.

This paper presents a step-up DC-DC converter that uses a stepwise gate-drive technique to reduce the power FET gate-drive energy by 82%, allowing positive efficiency down to an input voltage of ±0.5 mV—the lowest input voltage ever achieved for a DC-DC converter as far as we know. Below ±0.5 mV the converter automatically hibernates, reducing quiescent power consumption to just 255 pW. The converter has an efficiency of 63% at ±1 mV and 84% at ±6 mV. The input impedance is programmable from$1~\Omega $to$600~\Omega $to achieve maximum power extraction. A novel delay line circuit controls the stepwise gate-drive timing, programmable input impedance, and hibernation behavior. Bipolar input voltage is supported by using a flyback converter topology with two secondary windings. A generated power good signal enables the load when the output voltage has charged above 2.7 V and disables when the output voltage has discharged below 2.5 V. The DC-DC converter was used in a thermoelectric energy harvesting system that effectively harvests energy from small indoor temperature fluctuations of less than 1°C. Also, an analytical model with unprecedented accuracy of the stepwise gate-drive energy is presented.

During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates.

Connected devices are becoming more ubiquitous, but powering them remains a challenge. The Wireless Identification and Sensing Platform (WISP) is a fully programmable device capable of energy harvesting and backscatter communication. It can accommodate a variety of sensing modalities and operate without batteries or a wired power supply, making it a suitable device for ubiquitous computing. A new version of WISP is presented. WISP-6.0 is designed to be low-power, modular, and enable dual energy harvesting from sources like a solar panel. Additionally, an upgraded cross-platform host application is built using the latest web technologies. Compared to its predecessor, WISP-5.1, WISP-6.0 consumes 13.62% and 6.29% less power in active accelerometer and active acknowledgment modes respectively. Furthermore, WISP-6.0 is better able to harvest RF energy collected from its antenna, with the greatest improvements at higher input powers.

Brain–computer interfaces (BCIs) are neural prosthetics that enable closed-loop electrophysiology procedures. These devices are currently used in fundamental neurophysiology research, and they are moving toward clinical viability for neural rehabilitation. State-of-the-art BCI experiments have often been performed using tethered (wired) setups in controlled laboratory settings. Wired tethers simplify power and data interfaces but restrict the duration and types of experiments that are possible, particularly for the study of sensorimotor pathways in freely behaving animals. To eliminate tethers, there is significant ongoing research to develop fully wireless BCIs having wireless uplink of broadband neural recordings and wireless recharging for long-duration deployment, but significant challenges persist. BCIs must deliver complex functionality while complying with tightly coupled constraints in size, weight, power, noise, and biocompatibility. In this article, we provide an overview of recent progress in wireless BCIs and a detailed presentation of two emerging technologies that are advancing the state of the art: ultralow-power wireless backscatter communication and adaptive inductive resonant (AIR) wireless power transfer (WPT).

Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey article, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. In this domain, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Therefore, based on this observation, in the beginning of this article, we propose a categorization to organize the use cases of proximity sensors in human-centered robotics. Then, we devote effort to present the sensing technologies and different measuring principles that have been developed over the years, also providing a summary in form of a table. Following, we review the literature regarding the applications that have been proposed. Finally, we give an overview of the most important trends that will shape the future of this domain.

Realizing the vision of ubiquitous battery-free sensing has proven to be challenging, mainly due to the practical energy and range limitations of current wireless communication systems. To address this, we design the first wide-area and scalable backscatter network with multiple receivers (RX) and transmitters (TX) base units to communicate with battery-free sensor nodes. Our system circumvents the inherent limitations of backscatter systems -including the limited coverage area, frequency-dependent operability, and sensor node limitations in handling network tasks- by introducing several coordination techniques between the base units starting from a single RX-TX pair to networks with many RX and TX units.

We describe a single fingertip-mounted sensing system for robot manipulation that provides proximity (pre-touch), contact detection (touch), and force sensing (post-touch). The sensor system consists of optical time-of-flight range measurement modules covered in a clear elastomer. Because the elastomer is clear, the sensor can detect and range nearby objects, as well as measure deformations caused by objects that are in contact with the sensor and thereby estimate the applied force. We examine how this sensor design can be improved with respect to invariance to object reflectivity, signal-to-noise ratio, and continuous operation when switching between the distance and force measurement regimes. By harnessing time-of-flight technology and optimizing the elastomer-air boundary to control the emitted light's path, we develop a sensor that is able to seamlessly transition between measuring distances of up to 50 mm and contact forces of up to 10 newtons. We demonstrate that our sensor improves manipulation accuracy in a block unstacking task. Thorough instructions for manufacturing the sensor from inexpensive, commercially available components are provided, as well as all relevant hardware design files and software sources.

Ambient backscatter (AB), making use of both energy harvesting and backscattering, has recently become a promising solution to communications among low-power devices and demonstrates its potential application in the Internet of Things. Existing AB systems adopt two-state amplitude shift keying or phase shift keying (PSK), where data are transmitted at the rate of one bit per symbol period. To increase the data rate, we investigate the high-order modulation where M -PSK is employed for backscattering. We derive the optimal multilevel energy detector and compute the closed-form symbol error rate. To show the realizability of the proposed design, we build a 4PSK-AB hardware prototype, in which the selection of load impedance is discussed with the aid of phasor diagram illustration. The hardware prototype can achieve the date rate of 20 kb/s. Besides, higher date rate is achievable for 98.7% of the time compared with binary AB communications, and the mean number of distinguishable symbols is 3.66.

Wireless video streaming has traditionally been considered an extremely power-hungry operation. Existing approaches optimize the camera and communication modules individually to minimize their power consumption. However, designing a video streaming device requires power-consuming hardware components and video CODEC algorithms which makes battery-free video streaming currently infeasible. Existing RF-powered wireless camera prototypes require extensive duty-cycling on the order of tens of minutes, to capture, process and communicate a single frame. Self-powered cameras can capture an image once every few seconds, but do not have the capability to stream video wirelessly.

For more than two decades, capacitive sensing has played a prominent role in human-computer interaction research. Capacitive sensing has become ubiquitous on mobile, wearable, and stationary devices - enabling fundamentally new interaction techniques on, above, and around them. The research community has also enabled human position estimation and whole-body gestural interaction in instrumented environments. However, the broad field of capacitive sensing research has become fragmented by different approaches and terminology used across the various domains. This paper strives to unify the field by advocating consistent terminology and proposing a new taxonomy to classify capacitive sensing approaches. Our extensive survey provides an analysis and review of past research and identifies challenges for future work. We aim to create a common understanding within the field of human-computer interaction, for researchers and practitioners alike, and to stimulate and facilitate future research in capacitive sensing.

The primary focus of this work is to examine how robots can achieve more robust sequential manipulation through the use of pre-touch sensors. The utility of close-range proximity sensing is evaluated through a robotic system that uses a new optical time-of-flight pre-touch sensor to complete a highly precise and sequential task - solving the Rubik's cube. The techniques used in this task are then extended to a more general framework in which ICP is used to match pre-touch data to a reference model, demonstrating that even simple pre-touch scans can be used to recover the pose of common objects that require sequential manipulation.

We present Stork - an extension of the EPC C1G2 protocol allowing streaming of data to multiple Computational Radio Frequency IDentification tags (CRFIDs) simultaneously at up to 20 times faster than the prior state of the art. Stork introduces downstream attributes never before seen in (C)RFIDs: (i) fast feedback for CRFID downstream verification based on the internal EPC C1G2 memory check command - which we analytically and experimentally show to be the best possible downstream verification process based on EPC C1G2; (ii) ability to perform multi-CRFID transfer - which in our experiments speeds up downstream by more than two times compared to sequential transmission; and (iii) the use of compressed data streams - which improves firmware reprogramming times by up to 10% at large reader-to-CRFID distances.

For certain manipulation tasks, object pose estimation from head-mounted cameras may not be sufficiently accurate. This is at least in part due to our inability to perfectly calibrate the coordinate frames of today's high degree of freedom robot arms that link the head to the end-effectors. We present a novel framework combining pre-touch sensing and deep learning to more accurately estimate pose in an efficient manner. The use of pre-touch sensing allows our method to localize the object directly with respect to the robot's end effector, thereby avoiding error caused by miscalibration of the arms. Instead of requiring the robot to scan the entire object with its pre-touch sensor, we use a deep neural network to detect object regions that contain distinctive geometric features. By focusing pre-touch sensing on these regions, the robot can more efficiently gather the information necessary to adjust its original pose estimate. Our region detection network was trained using a new dataset containing objects of widely varying geometries and has been labeled in a scalable fashion that is free from human bias. This dataset is applicable to any task that involves a pre-touch sensor gathering geometric information, and has been made publicly available. We evaluate our framework by having the robot re-estimate the pose of a number of objects of varying geometries. Compared to two simpler region proposal methods, we find that our deep neural network performs significantly better. In addition, we find that after a sequence of scans, objects can typically be localized to within 0.5 cm of their true position. We also observe that the original pose estimate can often be significantly improved after collecting a single quick scan.

Computational RFID (CRFID) devices are emerging platforms that can enable perennial computation and sensing by eliminating the need for batteries. Although much research has been devoted to improving upstream (CRFID to RFID reader) communication rates, the opposite direction has so far been neglected, presumably due to the difficulty of guaranteeing fast and error-free transfer amidst frequent power interruptions of CRFID. With growing interest in the market where CRFIDs are forever-embedded in many structures, it is necessary for this void to be filled. Therefore, we propose Wisent — a robust downstream communication protocol for CRFIDs that operates on top of the legacy UHF RFID communication protocol: EPC C1G2. The novelty of Wisent is its ability to adaptively change the frame length sent by the reader, based on the length throttling mechanism, to minimize the transfer times at varying channel conditions. We present an implementation of Wisent for the WISP 5 and an off-the-shelf RFID reader. Our experiments show that Wisent allows transfer up to 16 times faster than a baseline, non-adaptive shortest frame case, i.e. single word length, at sub-meter distance. As a case study, we show how Wisent enables wireless CRFID reprogramming, demonstrating the world's first wirelessly reprogrammable (software defined) CRFID.

Optical depth cameras, including both time-of-flight and structured-light sensors, have led to dramatic improvements in robot sensing and perception. We propose the use of millimeter-wave (mmW) radar as an important complement to optical sensors. While the millimeter wavelengths of radar sensors do not support as high resolution as the nanometer wavelength of optical sensors, the ability of mmW signals to penetrate smoky and foggy environments as well as see through many opaque objects makes them a compelling sensor for navigation as well as manipulation in challenging environments. We present a series of 2D and 3D mmW images made with a hand-held antenna grasped by a PR2 robot. The radar image sensor uses a mechanical “painting” motion to acquire multiple views of the target object over the 15 - 26.5 GHz K-band. A GPU-based reconstruction algorithm synthesizes 2D and 3D images of the target object. We demonstrate a ground range resolution of 13.6 mm and a cross-range resolution of 7.1 mm for objects up to 0.5 m away from the robot. We further demonstrate imaging objects through fog, as well as through opaque paper.

Implantable neural recording and stimulation devices hold great promise in monitoring and treatment of neurological disorders, limb reanimation and, development of brain-computer interfaces among other applications. However, transcutaneous wires limit the lifetime of such devices and there is a need for self-contained fully implantable solutions. In this work, we propose a novel dual-frequency approach for simultaneous wireless power transfer and low-power communication for small form factor fully implantable neural devices. We deliver wireless power using efficient magnetically coupled resonators operating at 13.56MHz and communicate using ultra-low power backscatter communication at 915 MHz. We leverage the frequency separation to combine wireless power and communication resonators with minimal interference using a novel concentric design, which meets the stringent size restrictions. We implement the wireless power receiver and communication front end of the implanted device in 65 nm CMOS and demonstrate 25 mW power delivery and 6 Mbps communication link.

Neural stimulators are a key component of neural SoC designed for treating neurological disorders and closing the feedback loop in brain computer interfaces. In this work introduce a wireless power system customized to power a monolithic implantable neural (ECoG) stimulation platform which has unique power profile. For majority of time period, the stimulator is inactive and the SoC consumes minimal power (~100 μW). During an active stimulation period, a series of 100-200 μs long pulses of high current spaced apart by 2-3 ms are transmitted. This train of pulses is repeated every 200-400 ms. During the conduction of stimulation current, the peak power requirement can vary from 25 mW (1mA@ 12 V for 50% efficient stimulator) to 1 mW (200μA@12A for 50% efficient stimulator). So, in summary the wireless power system needs to power a load which can varies from 25 mW to 100 μW within 100 μs. These necessities a system which high dynamic range with high efficiencies and fast transient response.

We introduce inter-technology backscatter, a novel approach that transforms wireless transmissions from one technology to another, on the air. Specifically, we show for the first time that Bluetooth transmissions can be used to create Wi-Fi and ZigBee-compatible signals using backscatter communication. Since Bluetooth, Wi-Fi and ZigBee radios are widely available, this approach enables a backscatter design that works using only commodity devices.

We present the first power over Wi-Fi system that delivers power to low-power sensors and devices and works with existing Wi-Fi chipsets. Specifically, we show that a ubiquitous part of wireless communication infrastructure, the Wi-Fi router, can provide far field wireless power without significantly compromising the network's communication performance. Building on our design, we prototype battery-free temperature and camera sensors that we power with Wi-Fi at ranges of 20 and 17 feet respectively. We also demonstrate the ability to wirelessly trickle-charge nickel---metal hydride and lithium-ion coin-cell batteries at distances of up to 28 feet. We deploy our system in six homes in a metropolitan area and show that it can successfully deliver power via Wi-Fi under real-world network conditions without significantly degrading network performance.

RFID sensor networks perpetually stream sensor data without batteries. Cameras are power hungry but provide richer information than conventional sensor network nodes. Battery-free, RF-powered camera sensor nodes combine many of the attractive features of RFID sensor networks with those of cameras. However, prior battery-free cameras have no notion of 3D location, which is desirable for creating large scale networks of battery free cameras.

This paper presents a method of augmenting streaming point cloud data with pretouch proximity sensor information for the purposes of teleoperated grasping of transparent targets. When using commercial RGB-Depth (RGB-D) cameras, material properties can significantly affect depth measurements. In particular, transparent objects are difficult to perceive with RGB images and commercially available depth sensors. Geometric information of such objects needs to be gathered with additional sensors, and in many scenarios, it is of interest to gather this information without physical contact. In this work, a non-contact pretouch sensor fixed to the robot end effector is used to sense and explore physical geometries previously unobserved. Thus, the point cloud representation of an unknown, transparent grasp target, can be enhanced through telerobotic exploration in real-time. Furthermore, real-time haptic rendering algorithms and haptic virtual fixtures used in combination with the augmented streaming point clouds assist the teleoperator in collision avoidance during exploration. Theoretical analyses are performed to design virtual fixtures suitable for pretouch sensing, and experiments show the effectiveness of this method to gather geometry data without collision and eventually to successfully grasp a transparent object.

Robotic grasping has been hindered by the inability of robots to perceive unstructured environments. Because these environments can be complex or dynamic, it is important to obtain additional and precise sensing information just before grasping. This paper expands upon the pretouch modality by introducing a transmissive optical sensor. It can unambiguously indicate the presence or lack of objects in close proximity. A wide variety of items that other sensors fail to sense, such as extremely soft or shiny objects, can be detected by the proposed sensor. The sensor is also fully integrated into the fingertips of the PR2 robotic platform and manufactured with inexpensive, commercially-available components. Several experiments are conducted to verify its utility in both environment perception and robotic grasping. It is shown that the perception information supplied by the sensor facilitates effective robotic grasping.

In-band load modulation as a means of transferring information from a wirelessly-powered client device to a power provider in a wireless power transfer (WPT) system has long been considered to have an unacceptable impact on the efficiency of power transfer. Conventional load modulation involves either completely or severely detuning the receive coil in order to produce a detectable amplitude shift at the power provider side and thereby transfer information. In this work, we analyze the impact of load modulation on system efficiency by considering the power consumption of the transmit power amplifier (PA) and identify a tradeoff between communication capability and end-to-end wireless power transfer efficiency. We also determine how to select a load modulation strategy which reduces the degradation in power transfer efficiency while maintaining high-contrast uplink signaling. Finally, we explore the use of coherent demodulation (IQ demodulation) to reduce the mismatch requirement on a load modulation system by improving the power provider's sensitivity to load modulated signaling.

Indoor object localization can enable many ubicomp applications, such as asset tracking and object-related activity recognition. Most location and tracking systems rely on either battery-powered devices which create cost and maintenance issues or cameras which have accuracy and privacy issues. This paper introduces a system that is able to detect the 3D position and motion of a battery-free RFID tag embedded with an ultrasound detector and an accelerometer. Combining tags' acceleration with location improves the system's power management and supports activity recognition. We characterize the system's localization performance in open space as well as implement it in a smart wet lab application. The system is used to track real-time location and motion of the tags in the wet lab as well as recognize pouring actions performed on the objects to which the tag is attached. The median localization accuracy is 7.6cm -- (3.1, 5, 1.9)cm for each (x, y, z) axis -- with max update rates of 15 Sample/s using single RFID reader antenna.

Power remains a challenge in the widespread deployment of long-lived wireless sensing systems, which has led researchers to consider power harvesting as a potential solution. In this paper, we present a thermal power harvester that utilizes naturally changing ambient temperature in the environment as the power source. In contrast to traditional thermoelectric power harvesters, our approach does not require a spatial temperature gradient; instead it relies on temperature fluctuations over time, enabling it to be used freestanding in any environment in which temperature changes throughout the day. By mechanically coupling linear motion harvesters with a temperature sensitive bellows, we show the capability of harvesting up to 21 mJ of energy per cycle of temperature variation within the range 5 °C to 25 °C. We also demonstrate the ability to power a sensor node, transmit sensor data wirelessly, and update a bistable E-ink display after as little as a 0.25 °C ambient temperature change.

Onboard power remains a major challenge for miniature robotic platforms. Locomotion at small scales demands high power densities from all system components, while limited payload capacities place severe restrictions on the size of the energy source, resulting in integration challenges and short operating times when using conventional batteries. Wireless power delivery has the potential to allow microrobotic platforms to operate autonomously for extended periods when near a transmitter. This paper describes the first demonstration of RF wireless power transfer in an insect-scale ambulatory robot. A wireless power transmission system based on magnetically coupled resonance is designed for the latest iteration of the Harvard Ambulatory MicroRobot (HAMR), a piezoelectrically driven quadruped that had previously received power through a tether. Custom power and control electronics are designed and implemented on lightweight printed circuit boards that form a part of the mechanical structure of the robot. The integration of the onboard receiver, power and control electronics, and mechanical structure yields a 4cm, 2.1g robot that can operate autonomously in two wireless power transmission scenarios.

Wireless Power Transfer using magnetically coupled resonators has been widely used for biomedical and consumer electronics applications. However, typical analysis of wireless power transfer has focused on standard operating conditions (linear power source and fixed impedances Zs= ZL= Z0) and is not applicable for practical applications. In this work we use experimentally obtained S-parameters to accurately model and analyze a WPT system driven by a linear or non-linear power source with arbitrary source impedance (Zs) delivering power to an arbitrary load impedance (ZL). Using this approach, we define a framework to evaluate the performance of WPT in terms of two metrics i.e. efficiency and power delivered to the load. The proposed technique can help designers accurately predict and analyze the behavior of WPT systems in practical applications.

Optimization of coil sizes for wireless power transfer applications has a significant impact on the range and efficiency of the wireless power system. Often it is difficult to accurately analyze how a set of coils will perform before they are constructed due to the complexity of approximating the parasitic components and coupling coefficients associated with the coils. Also, for certain wireless power applications such as consumer electronic devices, implanted medical devices and industrial equipment that have physical constraints on the size and shape of the coils, it can be a difficult and time-consuming process to design and evaluate several different coil configurations for optimal range and efficiency. This paper provides simplified design equations to accurately calculate the self inductance, capacitance, resistance, quality factor and coupling coefficient in terms of coil geometries for flat, spiral coils. Experimental results validate the analysis and a design example is provided to optimize the size of a transmit coil for maximum range and wireless power efficiency to a 5.8cm receive coil used in a four-element wireless power transfer system.

RF-powered computers are small devices that compute and communicate using only the power that they harvest from RF signals. While existing technologies have harvested power from ambient RF sources (e.g., TV broadcasts), they require a dedicated gateway (like an RFID reader) for Internet connectivity. We present Wi-Fi Backscatter, a novel communication system that bridges RF-powered devices with the Internet. Specifically, we show that it is possible to reuse existing Wi-Fi infrastructure to provide Internet connectivity to RF-powered devices. To show Wi-Fi Backscatter's feasibility, we build a hardware prototype and demonstrate the first communication link between an RF-powered device and commodity Wi-Fi devices. We use off-the-shelf Wi-Fi devices including Intel Wi-Fi cards, Linksys Routers, and our organization's Wi-Fi infrastructure, and achieve communication rates of up to 1 kbps and ranges of up to 2.1 meters. We believe that this new capability can pave the way for the rapid deployment and adoption of RF-powered devices and achieve ubiquitous connectivity via nearby mobile devices that are Wi-Fi enabled.

Communication primitives such as coding and multiple antenna processing have provided significant benefits for traditional wireless systems. Existing designs, however, consume significant power and computational resources, and hence cannot be run on low complexity, power constrained backscatter devices. This paper makes two main contributions: (1) we introduce the first multi-antenna cancellation design that operates on backscatter devices while retaining a small form factor and power footprint, (2) we introduce a novel coding mechanism that enables long range communication as well as concurrent transmissions and can be decoded on backscatter devices. We build hardware prototypes of the above designs that can be powered solely using harvested energy from TV and solar sources. The results show that our designs provide benefits for both RFID and ambient backscatter systems: they enable RFID tags to communicate directly with each other at distances of tens of meters and through multiple walls. They also increase the communication rate and range achieved by ambient backscatter systems by 100X and 40X respectively. We believe that this paper represents a substantial leap in the capabilities of backscatter communication.

Paper displays have a number of attractive properties, in particular the ability to present visual information perpetually with no power source. However, they are not digitally updatable or re-usable. Bistable display materials, such as e-paper, promise to enable displays with the best properties of both paper and electronic displays. However, rewriting a pixelated bistable display requires substantial energy, both for communication and for setting the pixel states.

This paper presents a unified framework to enable automatic exploration with a pretouch sensor to reduce object shape uncertainty before grasping. The robot starts with only the incomplete object shape data it acquires from a Kinect depth sensor-it has no model of the object. Combining the Kinect pointcloud with prior probability distributions for occlusion and transparency, it makes inferences about portions of the object that the Kinect was not able to observe. Operating on the inferred shape of the object, an iterative grasp replanning and exploration algorithm decides when further exploration is required, and where to explore in the scene using the pretouch sensor. The information gathered by the exploration action is added directly to the robot's environment representation and is considered automatically in the next grasp planning iteration.

We present the design of a communication system that enables two devices to communicate using ambient RF as the only source of power. Our approach leverages existing TV and cellular transmissions to eliminate the need for wires and batteries, thus enabling ubiquitous communication where devices can communicate among themselves at unprecedented scales and in locations that were previously inaccessible.

Effective means of delivering wireless power to volumes of spaces will enable users the freedom and mobility to seamlessly power and recharge their devices in an unencumbered fashion. This has particular importance for consumer electronic, medical, and industrial applications, where usage models focus on unstructured and dynamic environments. However, existing wireless power technology falls short of this vision. Inductive charging solutions are limited to near-contact distances and require a docking station or precise placement for effective operation. Far-field wireless power techniques allow much greater range, but require complicated tracking systems to maintain a line-of-sight connection for high-efficiency power delivery to mobile applications. Recent work using magnetically coupled resonators (MCRs) for wireless power delivery has shown a promising intersection between range (on the order of a meter), efficiency (over 80%), and delivered power (up to tens of watts). However, unpredictable loads rapidly change system operating points, and changes in position disrupt system efficiency, which affects the ultimate usability of these systems. Dynamic adaptation to these changes in operating conditions and power transfer range is a critical capability in developing a fully functional and versatile wireless power solution. This paper provides an overview of methods used to adapt to variations in range, orientation, and load using both wideband and fixed-frequency techniques.

Wearable sensor systems have been used in the ubiquitous computing community and elsewhere for applications such as activity and gesture recognition, health and wellness monitoring, and elder care. Although the power consumption of accelerometers has already been highly optimized, this work introduces a novel sensing approach which lowers the power requirement for motion sensing by orders of magnitude. We present an ultra-low-power method for passively sensing body motion using static electric fields by measuring the voltage at any single location on the body. We present the feasibility of using this sensing approach to infer the amount and type of body motion anywhere on the body and demonstrate an ultra-low-power motion detector used to wake up more power-hungry sensors. The sensing hardware consumes only 3.3 μW, and wake-up detection is done using an additional 3.3 μW (6.6 μW total).

Recent advances in neuroscience and robotics have allowed initial demonstrations of brain-computer interfaces (BCIs) for controlling wheeled and humanoid robots. However, further advances have proved challenging due to the low throughput of the interfaces and the high degrees-of-freedom (DOF) of the robots. In this paper, we build on our previous work on Hierarchical BCIs (HBCIs) which seek to mitigate this problem. We extend HBCIs to allow training of arbitrarily complex tasks, with training no longer restricted to a particular robot state space (such as Cartesian space for a navigation task). We present two algorithms for learning command hierarchies by automatically extracting patterns from a user's command history. The first algorithm builds an arbitrary-level hierarchical structure (a “control grammar”) whose elements can represent skills, whole tasks, collections of tasks, etc. The user “executes” single symbols from this grammar, which produce sequences of lower-level commands. The second algorithm, which is probabilistic, also learns sequences which can be executed as high-level commands, but does not build an explicit hierarchical structure. Both algorithms provide a de facto form of dictionary compression, which enhances the effective throughput of the BCI. We present results from two human subjects who successfully used the hierarchical BCI to control a simulated PR2 robot using brain signals recorded non-invasively through electroencephalography (EEG).

Interaction with unstructured groups of objects allows a robot to discover and manipulate novel items in cluttered environments. We present a framework for interactive singulation of individual items from a pile. The proposed framework provides an overall approach for tasks involving operation on multiple objects, such as counting, arranging, or sorting items in a pile. A perception module combined with pushing actions accumulates evidence of singulated items over multiple pile interactions. A decision module scores the likelihood of a single-item pile to a multiple-item pile based on the magnitude of motion and matching determined from the perception module. Three variations of the singulation framework were evaluated on a physical robot for an arrangement task. The proposed interactive singulation method with adaptive pushing reduces the grasp errors on non-singulated piles compared to alternative methods without the perception and decision modules. This work contributes the general pile interaction framework, a specific method for integrating perception and action plans with grasp decisions, and an experimental evaluation of the cost trade-offs for different singulation methods.

This paper introduces seashell effect pretouch sensing, and demonstrates application of this new sensing modality to robot grasp control, and also to robot grasp planning.

Wireless data communication technology has eliminated wired connections for data transfer to portable devices. Wireless power technology offers the possibility of eliminating the remaining wired connection: the power cord. For ventricular assist devices (VADs), wireless power technology will eliminate the complications and infections caused by the percutaneous wired power connection. Integrating wireless power technology into VADs will enable VAD implants to become a more viable option for heart failure patients (of which there are 80 000 in the United States each year) than heart transplants. Previous transcutaneous energy transfer systems (TETS) have attempted to wirelessly power VADs ; however, TETS-based technologies are limited in range to a few millimeters, do not tolerate angular misalignment, and suffer from poor efficiency. The free-range resonant electrical delivery (FREE-D) wireless power system aims to use magnetically coupled resonators to efficiently transfer power across a distance to a VAD implanted in the human body, and to provide robustness to geometric changes. Multiple resonator configurations are implemented to improve the range and efficiency of wireless power transmission to both a commercially available axial pump and a VentrAssist centrifugal pump [3]. An adaptive frequency tuning method allows for maximum power transfer efficiency for nearly any angular orientation over a range of separation distances. Additionally, laboratory results show the continuous operation of both pumps using the FREE-D system with a wireless power transfer efficiency upwards of 90%.

We present a novel and simple experimental method called physical human interactive guidance to study human-planned grasping. Instead of studying how the human uses his/her own biological hand or how a human teleoperates a robot hand in a grasping task, the method involves a human interacting physically with a robot arm and hand, carefully moving and guiding the robot into the grasping pose, while the robot's configuration is recorded. Analysis of the grasps from this simple method has produced two interesting results. First, the grasps produced by this method perform better than grasps generated through a state-of-the-art automated grasp planner. Second, this method when combined with a detailed statistical analysis using a variety of grasp measures (physics-based heuristics considered critical for a good grasp) offered insights into how the human grasping method is similar or different from automated grasping synthesis techniques. Specifically, data from the physical human interactive guidance method showed that the human-planned grasping method provides grasps that are similar to grasps from a state-of-the-art automated grasp planner, but differed in one key aspect. The robot wrists were aligned with the object's principal axes in the human-planned grasps (termed low skewness in this paper), while the automated grasps used arbitrary wrist orientation. Preliminary tests show that grasps with low skewness were significantly more robust than grasps with high skewness (77-93%). We conclude with a detailed discussion of how the physical human interactive guidance method relates to existing methods to extract the human principles for physical interaction.

This paper presents Gambit, a custom, mid-cost 6-DoF robot manipulator system that can play physical board games against human opponents in non-idealized environments. Historically, unconstrained robotic manipulation in board games has often proven to be more challenging than the underlying game reasoning, making it an ideal testbed for small-scale manipulation. The Gambit system includes a low-cost Kinect-style visual sensor, a custom manipulator, and state-of-the-art learning algorithms for automatic detection and recognition of the board and objects on it. As a use-case, we describe playing chess quickly and accurately with arbitrary, uninstrumented boards and pieces, demonstrating that Gambit's engineering and design represent a new state-of-the-art in fast, robust tabletop manipulation.

Humans are adept at grasping different objects robustly for different tasks. Robotic grasping has made significant progress, but still has not reached the level of robustness or versatility shown by human grasping. It would be useful to understand what parameters (called grasp measures) humans optimize as they grasp objects, how these grasp measures are varied for different tasks, and whether they can be applied to physical robots to improve their robustness and versatility. This paper demonstrates a new way to gather human-guided grasp measures from a human interacting haptically with a robotic arm and hand. The results revealed that a human-guided strategy provided grasps with higher robustness on a physical robot even under a vigorous shaking test (91%) when compared with a state-of-the-art automated grasp synthesis algorithm (77%). Furthermore, orthogonality of wrist orientation was identified as a key human-guided grasp measure, and using it along with an automated grasp synthesis algorithm improved the automated algorithm's results dramatically (77% to 93%).

We describe a robot that is able to autonomously plug itself in to standard, unmodified electrical outlets by sensing the 60Hz electric fields emitted from the outlet. The building electrical infrastructure is not modified in any way. Unlike previous powerline localization work, no additional signal is injected in the powerlines-the already present AC power carrier signal in the outlet is used as the localization beacon. This technique is faster, more accurate, and potentially less expensive than previously reported vision-based systems for autonomous plugging in.

Pretouch sensing is longer range than contact, but shorter range than vision. The hypothesis motivating this work is that closed loop feedback based on short range but non-contact measurements can improve the reliability of manipulation. This paper presents a grasping system that is guided at short range by Electric Field (EF) Pretouch. We describe two sets of experiments. The first set of experiments involves human-to-robot and robot-to-human handoff, including the use of EF Pretouch to detect whether or not a human is also touching an object that the robot is holding, which we call the “co-manipulation state.” In the second set of experiments, the robot picks up standalone objects. We describe a number of techniques that servo the arm and fingers in order to both collect relevant geometrical information, and to actually perform the manipulation task.

The next generation internet will be the internet of things (and not just of computing devices like PCs, PDAs); this is presumed to be enabled by integrating simple computing plus communications capabilities into common objects of everyday use. Radio-frequency identification (RFID) is a compelling technology for creation of such pervasive sensor networks due to its potential for ubiquitous, low-cost/low-maintenance use. However, the current drivers for RFID deployment emphasize supply chain management using passive tags, implying that RFID sensor nets require advances beyond the components and system designs aimed at supply chain applications. This work provides a glimpse of how this may be achieved.

Future homes will be populated with large numbers of robots with diverse functionalities, ranging from chore robots to elder care robots to entertainment robots. While household robots will offer numerous benefits, they also have the potential to introduce new security and privacy vulnerabilities into the home. Our research consists of three parts. First, to serve as a foundation for our study, we experimentally analyze three of today's household robots for security and privacy vulnerabilities: the WowWee Rovio, the Erector Spykee, and the WowWee RoboSapien V2. Second, we synthesize the results of our experimental analyses and identify key lessons and challenges for securing future household robots. Finally, we use our experiments and lessons learned to construct a set of design questions aimed at facilitating the future development of household robots that are secure and preserve their users' privacy.

We tackle the problem of defending against ghost-and-leech (a.k.a. proxying, relay, or man-in-the-middle) attacks against RFID tags and other contactless cards. The approach we take -- which we dub secret handshakes -- is to incorporate gesture recognition techniques directly on the RFID tags or contactless cards. These cards will only engage in wireless communications when they internally detect these secret handshakes. We demonstrate the effectiveness of this approach by implementing our secret handshake recognition system on a passive WISP RFID tag with a built-in accelerometer. Our secret handshakes approach is backward compatible with existing deployments of RFID tag and contactless card readers.

This paper presents two experiments with electric field servoing for robotic manipulation. In the first, a robot hand pre-shapes to the geometry and pose of objects to be grasped, by servoing each finger according to the values on EF sensors built in to each finger. In the second, a 7 degree of freedom arm aligns itself in 2 dimensions with a target object using electric field measurements as the error signal. This system also allows the end effector to dynamically track the target object as it is moved.

We demonstrate a simple RFID sensor network comprised of an Intel WISP and a commodity UHF RFID reader. WISPs are devices that gather their operating energy from RFID reader transmissions, in the manner of passive RFID tags, and further include sensors, e.g., accelerometers, and provide a very small-scale computing platform. We believe that the small form factor and lack of battery makes the WISP an attractive alternative to motes for many of the original smart dust applications that require very small or long-lived sensors. The Intel WISP that we demonstrate has an ultra-low-power microcontroller, 32K of program space, 8K of flash, and accelerometer and temperature sensors. It harvests power from and communicates sensor data to standard (EPC Class 1 Gen 2) UHF RFID readers with a range of roughly 10 feet. This combination of RFID technology and sensor networks raises many research challenges, such as how to function with intermittent power and how to modify RFID protocols to support sensor queries.

The relevant performance metric for successful deployment of radio frequency identification (RFID) systems for tag inventory applications is the latency for reading all tags with (high) reliability. Tag collisions in response to a reader query increase the read latency of the MAC protocol; the mean latency can be considerably improved by a combination of techniques including more efficientanti-collisionapproaches as well as via estimationofthenumberofbackloggedtags. We propose a novel anti-collision algorithm:breadth-first-searchwithm-arysplitting(BMSA) within a TDMA frame structure. A simple backlogged tag estimation algorithm is used in conjunction with the above to dynamically set thesplittingfactor(SF) m. Simulation results demonstrate the superiority of the proposed scheme over existing methods in terms of throughput/latency.

This paper proposes the use of electric field sensors to implement "pretouch" for robotic grasping. Weakly electric fish use this perceptual channel, but it has not received much attention in robotics. This paper describes a series of manipulators each of which incorporates electric field sensors in a different fashion. In each case, the paper presents techniques for using the sensors, and experimental data collected. First, a simple dynamic-object avoidance technique is presented. Next, a 1-D alignment task for grasping is described. Then linear and rotary electrode scanning techniques are presented. It is shown that these techniques can distinguish a small object at close range from a large object farther away, a capability that may be important for grasping.

This paper demonstrates the use of software radio techniques in the context of sensing, rather than communications. It describes code-division multiplexing (CDMA) and time-division multiplexing (TDMA) of a receiver channel in an electric field sensing system. The only hardware used is a front-end gain stage consisting of two opamps and a microcontroller. The modulation and demodulation operations are implemented entirely in the microcontroller software. Multiple coded waveforms are transmitted simultaneously, and induce a combined signal on a single receive electrode. The combined signal, after passing through a single analog front end terminating in an analog-to-digital converter, is separated into the four original component signals by a software demodulation operation. The signal-to-noise ratio (SNR) achieved by the code-division multiplexed system given a fixed measurement time is compared to the SNR achieved by a time-division multiplexed implementation given the same total measurement time. The paper also compares the scaling of TDMA and CDMA performance with the number of transmitted channels and the number of demodulated channels.

A non-contact sensor based on the interaction of a person with electric fields for human-computer interface is investigated. Two sensing modes are explored: an external electric field shunted to ground through a human body, and an external electric field transmitted through a human body to stationary receivers. The sensors are low power (milliwatts), high resolution (millimeter) low cost (a few dollars per channel), have low latency (millisecond), high update rate (1 kHz), high immunity to noise (>72 dB), are not affected by clothing, surface texture or reflectivity, and can operate on length scales from microns to meters. Systems incorporating the sensors include a finger mouse, a room that knows the location of its occupant, and people-sensing furniture. Haptic feedback using passive materials is described. Also discussed are empirical and analytical approaches to transform sensor measurements into position information. KEYWORDS: user interface, input device, gesture interface, non-...