Efficient Human-Robot Interaction requires flexible role switching and robust role distribution. Leader and follower agents are mostly recognized based on interaction forces. This often comes with the drawback of reduced robustness and the necessity of models of the robot's environment or human be-haviour. This video presents the recently proposed Elasto-Plastic Robot Compliance (EPRC) concept that, being power-based, additionally considers the robot velocity in role recognition. Thus, active environments can be detected inducing enhanced virtual plastic compliance of the robot. Such plastic compliance refers to evasive robot motions induced specifically by active environments, after which the robot is not pushed back to the initial point of contact in contrast to elastic compliance. The video further presents advantages as role distribution specific to different degrees-of-freedom, independence from model generation and unrestricted force application.

In this paper we study a worst case to average case reduction for the problem of matrix multiplication over finite fields. Suppose we have an efficient average case algorithm, that given two random matrices $A,B$ outputs a matrix that has a non-trivial correlation with their product $A \cdot B$. Can we transform it into a worst case algorithm, that outputs the correct answer for all inputs without incurring a significant overhead in the running time? We present two results in this direction. (1) Two-sided error in the high agreement regime: We begin with a brief remark about a reduction for high agreement algorithms, i.e., an algorithm which agrees with the correct output on a large (say $>0.9$) fraction of entries, and show that the standard self-correction of linearity allows us to transform such algorithms into algorithms that work in worst case. (2) One-sided error in the low agreement regime: Focusing on average case algorithms with one-sided error, we show that over $\mathbb{F}_2$ there is a reduction that gets an $O(T)$ time average case algorithm that given a random input $A,B$ outputs a matrix that agrees with $A \cdot B$ on at least $51\%$ of the entries (i.e., has only a slight advantage over the trivial algorithm), and transforms it into an $\widetilde{O}(T)$ time worst case algorithm, that outputs the correct answer for all inputs with high probability.

In this work, we present a learning from demonstration solution for automating a surgical blunt dissection task. In addition to learning motion trajectories, our goal is to learn variable impedance behaviors that enable the robot to interact safely and compliantly during the task. To that end, we propose a teaching interface using bilateral teleoperation, which allows the natural transfer of human motions and impedance behaviors skills to robots. The demonstrated profiles are captured with Dynamic Movement Primitives and Gaussian Mixture Models, which subsequently provide the robot a reference motion plan, and a stiffness adaptation policy, during physical interaction. Experimental validation on real robot hardware shows the effectiveness of the proposed approach in terms of ensuring successful task execution, as well as safety compared to stiff high-gain control.

Software defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowdsourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address these needs, we design and develop a compact, portable, untethered, and inexpensive SDR we callSitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. We present results from a user study involving more than 100 participants to evaluate Sitara in a hypothetical large-scale crowdsourced spectrum monitoring application. We also present a comparative analysis of Sitara to related crowdsensing systems with a particular emphasis on the role of incentives and user participation.

Attaching a robotic manipulator to a flying base allows for significant improvements in the reachability and versatility of manipulation tasks. In order to explore such systems while taking advantage of human capabilities in terms of perception and cognition, bilateral teleoperation arises as a reasonable solution. However, since most telemanipulation tasks require visual feedback in addition to the haptic one, real-time (task-dependent) positioning of a video camera, which is usually attached to the flying base, becomes an additional objective to be fulfilled. Since the flying base is part of the kinematic structure of the robot, if proper care is not taken, moving the video camera could undesirably disturb the end-effector motion. For that reason, the necessity of controlling the base position in the null space of the manipulation task arises. In order to provide the operator with meaningful information about the limits of the allowed motions in the null space, this paper presents a novel haptic concept called Null-Space Wall. In addition, a framework to allow stable bilateral teleoperation of both tasks is presented. Numerical simulation data confirm that the proposed framework is able to keep the system passive while allowing the operator to perform time-delayed telemanipulation and command the base to a task-dependent optimal pose.

For safe human-robot interaction, various type of flexible manipulators have been developed. Especially series elastic actuator (SEA) based manipulators have been getting huge attention since the elastic element of SEA prevents people from injury when undesirable collision happens. Moreover, it improves system durability by absorbing impact force, which could damage actuators. However, the elastic element inside SEA manipulator causes low system bandwidth which limits the speed performance of conventional impedance control approaches. To alleviate the low bandwidth issue of impedance controlled SEA while guaranteeing system stability, we implement Time Domain Passivity Approach (TDPA) and Successive Stiffness Increment (SSI) approach, which was invented in haptic and teleoperation domain. Impedance controlled SEA is reformulated as a two-port electrical circuit network for implementing TDPA. In addition, a pair of input and output power conjugate variable, dominating the system passivity is identified for implementing SSI approach. Experimental results showed that TDPA and SSI approach can render the stiffness of the impedance controller, which decides the bandwidth, upto 350 kN/m without any stability issue, while normal impedance controller only render upto 120 kN/m. Although both of the approaches significantly increased the bandwidth of the impedance controlled SEA, TDPA slightly outperformed in stability, and SSI outperformed in tracking.

We present LLOCUS, a novel learning-based system that uses mobile crowdsourced RF sensing to estimate the location and power of unknown mobile transmitters in real time, while allowing unrestricted mobility of the crowdsourcing participants. We carefully identify and tackle several challenges in learning and localizing, based on RSS, in such a dynamic environment. We decouple the problem of localizing a transmitter with unknown transmit power into two problems, 1) predicting the power of a transmitter at an unknown location, and 2) localizing a transmitter with known transmit power. LLOCUS first estimates the power of the unknown transmitter and then scales the reported RSS values such that the unknown transmit power problem is transparent to the method of localization. We evaluate LLOCUS using three experiments in different indoor and outdoor environments. We find that LLOCUS reduces the localization error by 17-68% compared to several non-learning methods.

Traffic congestion on urban roadways is a serious problem requiring novel ways to detect and mitigate it. Determining the routes that lead to the traffic congestion segment is also vital in devising mitigation strategies. Further, crowdsourcing this information allows for use of these strategies quickly and in places where infrastructure is not available. In this work, we present an unconventional method, using the barometer sensor of mobile phones to (a) detect road traffic congestion and (b) estimate the paths that lead to the congested road segment. We make the observation that roads are not completely flat and very often, altitude varies along the road. The barometer sensor chips are sensitive enough to measure these variations and consume very little energy of the mobile phone, compared to other sensors such as the GPS or accelerometer. We devise a feature set to map the rate of change of this altitude as the user moves into activities characterized as “still” and “motion,” which are further used by the traffic congestion detection algorithm (RoadSphygmo) to classify the group of users as being in “moving,” “congestion,” or “stuck” states. To estimate the paths that lead to the congested road segment, we compare the user’s barometer sensor readings with a pre-stored road signature of barometer values using Dynamic Time Warping (DTW). We show that by using correlation of barometer sensor values, we can determine if users are in the same vehicle. We crowdsource this information from multiple mobile phones and use majority voting technique to improve the accuracy of traffic congestion detection and path estimation. We find a significant increase in the accuracies using crowdsourced information as compared to individual mobile phones. Further, we show that we can use barometer sensor for other applications such as bus occupancy, boarding/deboarding of a vehicle, and so on. The validation of the state determined by RoadSphygmo is done by comparing it with average GPS speed calculated during the same time period. The path estimation is validated over different intersections and considering various cases of commuter travel. The results obtained are promising and show that the traffic state determination and the estimation of the path taken by the commuter can achieve high accuracy.

Passivity has been the most often used constraint for the stable controller design of bilateral teleoperation systems. Especially, Time Domain Passivity Approach (TDPA) has been used in many applications since it has been known as one of the least conservative passivity-based approaches. Although TDPA were able to stabilize the system with the least conservatism, it has its own drawbacks as the cost of achieving the least conservative passivity especially when there is communication time-delay. Due to the on/off bang-bang control-like modification for instantaneous passivity recovery, it has high frequency force vibrations on the slave and especially master side. By implementing a virtual mass-spring system between the passivity controller and master device, these high frequency vibration has been eliminated. However, the gains need proper tuning as they are dependent on the teleoperation setup and application. It also tends to make the system sluggish which further distorts the transparency. We propose a new observer-based gradient controller to eliminate the force jittering on the master side. It rectifies the delayed feedback force by removing the undesired increase in force which is generated by the delay in communication channel. It does not require any system parameters and there are no gains to tune, thus it can be added to any teleoperator irrespective of its dynamics and without having any prior system information. The proposed approach was implemented to a position-force bilateral teleoperation system, and compared with TDPA with virtual mass spring with round-trip delays of up to 500 ms for hard wall contacts.

Software-defined radios (SDRs) are often used in the experimental evaluation of next-generation wireless technologies. While crowd-sourced spectrum monitoring is an important component of future spectrum-agile technologies, there is no clear way to test it in the real world, i.e., with hundreds of users each carrying an SDR while uploading data to a cloud-based controller. Current fully functional SDRs are bulky, with components connected via wires, and last at most hours on a single battery charge. To address the needs of such experiments, we design and develop a compact, portable, untethered, and inexpensive SDR we call Sitara. Our SDR interfaces with a mobile device over Bluetooth 5 and can function standalone or as a client to a central command and control server. The Sitara offers true portability: it operates up to one week on battery power, requires no external wired connections and occupies a footprint smaller than a credit card. It transmits and receives common waveforms, uploads IQ samples or processed receiver data through a mobile device to a server for remote processing and performs spectrum sensing functions. Multiple Sitaras form a distributed system capable of conducting experiments in wireless networking and communication in addition to RF monitoring and sensing activities. In this paper, we describe our design, evaluate our solution, present experimental results from multi-sensor deployments and discuss the value of this system in future experimentation.

Despite being one of the most robust methods in bilateral teleoperation, Time Domain Passivity Approach (TDPA)presents the drawback of accumulating position drift between master and slave devices. The lack of position synchronization poses an obstacle to the performance of teleoperation and may prevent the successful accomplishment of such tasks. Several techniques have been developed in order to solve the position-drift problem in TDPA-based teleoperation. However, they either present poor transparency by over-conservatively constraining force feedback or add high impulse-like force signals that can be harmful to the hardware and to the human operator. We propose a new approach to compensate position drift in TDPA-based teleoperation in a smoother way, which keeps the forces within the normal range of the teleoperation task while preserving the level of transparency and the robust stability of energy-based TDPA. We also add a way of tuning the compensator to behave in accordance with the task being performed, whether it requires faster or smoother compensation. The feasibility and performance of the method were experimentally validated. Good position tracking and regular-amplitude forces are demonstrated with up to 500 ms round-trip constant and variable delays for hard-wall contacts.

Enhancing transparency of a teleoperation system by increasing the command-following bandwidth has not received lots of attention so far. This is considered a challenging task since in a teleoperation system the command-following bandwidth of the slave robot motion controller cannot be increased with a conventional motion controller as the desired trajectory is instantaneously commanded by the human user and thus, cannot be considered to be given in a pre-computed, smooth second order derivative form. We propose a method to increase the command-following bandwidth by extending the previously introduced Successive Stiffness Increment (SSI) approach to bilateral teleoperation. The approach allows realizing a very high motion controller gain, which cannot be realized with a conventional bilateral teleoperation controller as confirmed by experimental results.

Despite the substantial progression of autonomous driving systems, their application is often limited e.g. due to safety margins which can be caused by uncertainties in the environment reconstruction. Then, via teleoperation as a fallback solution, a human-in-the-loop can be introduced as the main decision maker. However, high delay in the communication channel distorts the performance of direct force feedback teleoperation for example in space or disaster scenarios. On the other hand, model-mediated teleoperation can provide instantaneous and even predictive force feedback to the user, but the performance is limited due to state mismatches, incomplete models, model errors and the modeling challenges of complex wheel-ground contacts. Therefore, in this paper we introduce the concept of extended model-mediated teleoperation with a car like interface for mobile robots by fusing local fictitious and remote force feedback, which can be measured, computed or fictitious. We provide a method to guarantee stability of the extended model-mediated teleoperation (involving time delay, multilateral coupling, fictitious force feedback and permanent updates of the local model) based on the passivity theorem. The benefits of the approach are highlighted by human-in-the-loop experiments with a wheeled mobile robot.

Many networked users through their devices are interested in participating in distributed sensing and data collection for the purpose of betterment of human society or for earning rewards. Preservation of their location privacy is an important requirement for users participating and contributing to the data collection. We develop a novel privacy preserving approach for collecting noise-free data from vehicular users. Collection of noise-free, "pure" data, enhances its utility in the applications that use it. Location privacy must be preserved from the entity that we call a central controller, that collects all the vehicular data, and is assumed to be adversarial. We collect the data in a noise-free form by introducing temporal and spatial variations using Random Delays and Indirections. We run simulations using network and vehicle simulators driven by a real-world traffic scenario from the city of Luxembourg to evaluate our approach. Our simulation results show that the adversary cannot localize the uploaders within the thresholds of the number of streets and the length of the region of interest chosen by them.

Actuators with adjustable transmission ratios are required in a variety of robotics and automation applications, spanning from human assistive devices and mobile robots to general manipulators. Most existing self-adjustable actuators are bulky and mechanically complex, which often makes their implementation challenging. In this work, we propose a novel passively-adjustable transmission mechanism based on a twisted string actuator. Twisted string actuator is a light, cheap, and mechanically simple actuator, in which twisted strings contract as a result of twisting and therefore act as a translational gear. If one introduces a physical offset between the twisted strings, this changes a ratio between the speed and output force provided by actuator. This work introduces a kinematical model of such actuator for the configuration when a physical offset between the strings is present. In order to experimentally verify the proposed mathematical model, we designed and manufactured a twisted string actuator with variable offsets between the strings. Additionally, in this paper we also propose an idea for passively adjustable gear, controlled by twisted string actuator according to the proposed kinematical model. The main advantages of the proposed passively adjustable gear are its light weight, compliancy and mechanical simplicity, which make it attractive for implementaion in various areas of haptics, teleoperation, wearable and lightweight robotics.