Challenges in Collaborative HRI for Remote Robot Teams

Collaboration between human supervisors and remote teams of robots is highly challenging, particularly in high-stakes, distant, hazardous locations, such as off-shore energy platforms. In order for these teams of robots to truly be beneficial, they need to be trusted to operate autonomously, performing tasks such as inspection and emergency response, thus reducing the number of personnel placed in harm's way. As remote robots are generally trusted less than robots in close-proximity, we present a solution to instil trust in the operator through a `mediator robot' that can exhibit social skills, alongside sophisticated visualisation techniques. In this position paper, we present general challenges and then take a closer look at one challenge in particular, discussing an initial study, which investigates the relationship between the level of control the supervisor hands over to the mediator robot and how this affects their trust. We show that the supervisor is more likely to have higher trust overall if their initial experience involves handing over control of the emergency situation to the robotic assistant. We discuss this result, here, as well as other challenges and interaction techniques for human-robot collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019 Workshop: The Challenges of Working on Social Robots that Collaborate with People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing Systems, May 2019, Glasgow, U

PILOT : Practical Privacy-Preserving Indoor Localization Using OuTsourcing

In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends. Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS. In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography. We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics. To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties. Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL. We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size. Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction. Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500× faster than that of Ziegeldorf et al. (WiSec'14). Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day.Peer reviewe

Affordance-Aware Handovers With Human Arm Mobility Constraints

Reasoning about object handover configurations allows an assistive agent to estimate the appropriateness of handover for a receiver with different arm mobility capacities. While there are existing approaches for estimating the effectiveness of handovers, their findings are limited to users without arm mobility impairments and to specific objects. Therefore, current state-of-the-art approaches are unable to hand over novel objects to receivers with different arm mobility capacities. We propose a method that generalises handover behaviours to previously unseen objects, subject to the constraint of a user's arm mobility levels and the task context. We propose a heuristic-guided hierarchically optimised cost whose optimisation adapts object configurations for receivers with low arm mobility. This also ensures that the robot grasps consider the context of the user's upcoming task, i.e., the usage of the object. To understand preferences over handover configurations, we report on the findings of an online study, wherein we presented different handover methods, including ours, to $259$ users with different levels of arm mobility. We find that people's preferences over handover methods are correlated to their arm mobility capacities. We encapsulate these preferences in a statistical relational model (SRL) that is able to reason about the most suitable handover configuration given a receiver's arm mobility and upcoming task. Using our SRL model, we obtained an average handover accuracy of $90.8\%$ when generalising handovers to novel objects.Comment: Accepted for RA-L 202

Iron biogeochemistry across marine systems progress from the past decade

Based on an international workshop (Gothenburg, 14–16 May 2008), this review article aims to combine interdisciplinary knowledge from coastal and open ocean research on iron biogeochemistry. The major scientific findings of the past decade are structured into sections on natural and artificial iron fertilization, iron inputs into coastal and estuarine systems, colloidal iron and organic matter, and biological processes. Potential effects of global climate change, particularly ocean acidification, on iron biogeochemistry are discussed. The findings are synthesized into recommendations for future research areas

Autoencoder extreme learning machine for fingerprint-based positioning: A good weight initialization is decisive

Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use k -nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline

Parasitic Dinoflagellate \u3ci\u3eHematodinium perezi\u3c/i\u3e Prevalence in Larval and Juvenile Blue Crabs \u3ci\u3eCallinectes sapidus\u3c/i\u3e from Coastal Bays of Virginia

The parasitic dinoflagellate Hematodinium perezi infects the American blue crab Callinectes sapidus and other decapods along the Eastern seaboard and Gulf of Mexico coast of the USA. Large juvenile and adult blue crabs experience high mortality during seasonal outbreaks of H. perezi, but less is known about its presence in the early life history stages of this host. We determined the prevalence of H. perezi in megalopae and early benthic juvenile crabs from multiple locations along the Virginia portion of the Delmarva Peninsula. The DNA of H. perezi was not detected in any megalopae collected from several locations within the oceanic coastal bay complex in which H. perezi is found at high prevalence levels. However, prevalence levels were high in early benthic juveniles from 2 oceanic coastal embayments: South Bay and Cobb Bay. Prevalence levels were lower at locations within Chesapeake Bay, including Cherrystone Creek, Hungars Creek, and Pungoteague Creek. Sampling over different seasons and several consecutive years indicates that disease transmission occurs rapidly after megalopae settle in high-salinity bays along the Delmarva Peninsula during the late summer and fall. Infected juvenile crabs can overwinter with the parasite and, when subjected to increasing water temperatures in spring, infections progress rapidly, culminating in transmission to other crabs in late spring and early summer. In high-salinity embayments, H. perezi can reach high prevalence levels and may significantly affect recruitment of juvenile blue crabs into the adult fishery

The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces

We present the UK Robotics and Artificial Intelligence Hub for Offshore Robotics for Certification of Assets (ORCA Hub), a 3.5 year EPSRC funded, multi-site project. The ORCA Hub vision is to use teams of robots and autonomous intelligent systems (AIS) to work on offshore energy platforms to enable cheaper, safer and more efficient working practices. The ORCA Hub will research, integrate, validate and deploy remote AIS solutions that can operate with existing and future offshore energy assets and sensors, interacting safely in autonomous or semi-autonomous modes in complex and cluttered environments, co-operating with remote operators. The goal is that through the use of such robotic systems offshore, the need for personnel will decrease. To enable this to happen, the remote operator will need a high level of situation awareness and key to this is the transparency of what the autonomous systems are doing and why. This increased transparency will facilitate a trusting relationship, which is particularly key in high-stakes, hazardous situations.Comment: 2 pages. Peer reviewed position paper accepted in the Explainable Robotic Systems Workshop, ACM Human-Robot Interaction conference, March 2018, Chicago, IL US

Biogeochemical cycling of dissolved zinc along the GEOTRACES South Atlantic transect GA10 at 40°S

The biogeochemical cycle of zinc (Zn) in the South Atlantic, at 40°S, was investigated as part of the UK GEOTRACES program. To date there is little understanding of the supply of Zn, an essential requirement for phytoplankton growth, to this highly productive region. Vertical Zn profiles displayed nutrient-like distributions with distinct gradients associated with the watermasses present. Surface Zn concentrations are among the lowest reported for theworld’s oceans (<50 pM). A strong Zn-Si linear relationshipwas observed (Zn (nM)= 0.065 Si (μM), r2=0.97, n = 460). Our results suggest that the use of a global Zn-Si relationship would lead to an underestimation of dissolved Zn in deeper waters of the South Atlantic. By utilizing Si* and a new tracer Zn* our data indicate that the preferential removal of Zn in the Southern Ocean prevented a direct return path for dissolved Zn to the surface waters of the South Atlantic at 40°S and potentially the thermocline waters of the South Atlantic subtropical gyre. The importance of Zn for phytoplankton growth was evaluated using the Zn-soluble reactive phosphorus (SRP) relationship. We hypothesize that the low Zn concentrations in the South Atlantic may select for phytoplankton cells with a lower Zn requirement. In addition, a much deeper kink at ~ 500m in the Zn:SRP ratio was observed compared to other oceanic regions