496 research outputs found
In-home and remote use of robotic body surrogates by people with profound motor deficits
By controlling robots comparable to the human body, people with profound
motor deficits could potentially perform a variety of physical tasks for
themselves, improving their quality of life. The extent to which this is
achievable has been unclear due to the lack of suitable interfaces by which to
control robotic body surrogates and a dearth of studies involving substantial
numbers of people with profound motor deficits. We developed a novel, web-based
augmented reality interface that enables people with profound motor deficits to
remotely control a PR2 mobile manipulator from Willow Garage, which is a
human-scale, wheeled robot with two arms. We then conducted two studies to
investigate the use of robotic body surrogates. In the first study, 15 novice
users with profound motor deficits from across the United States controlled a
PR2 in Atlanta, GA to perform a modified Action Research Arm Test (ARAT) and a
simulated self-care task. Participants achieved clinically meaningful
improvements on the ARAT and 12 of 15 participants (80%) successfully completed
the simulated self-care task. Participants agreed that the robotic system was
easy to use, was useful, and would provide a meaningful improvement in their
lives. In the second study, one expert user with profound motor deficits had
free use of a PR2 in his home for seven days. He performed a variety of
self-care and household tasks, and also used the robot in novel ways. Taking
both studies together, our results suggest that people with profound motor
deficits can improve their quality of life using robotic body surrogates, and
that they can gain benefit with only low-level robot autonomy and without
invasive interfaces. However, methods to reduce the rate of errors and increase
operational speed merit further investigation.Comment: 43 Pages, 13 Figure
Scaled Autonomy for Networked Humanoids
Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework.
The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment.
Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC
Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges
Human-swarm interaction (HSI) involves a number of human factors impacting
human behaviour throughout the interaction. As the technologies used within HSI
advance, it is more tempting to increase the level of swarm autonomy within the
interaction to reduce the workload on humans. Yet, the prospective negative
effects of high levels of autonomy on human situational awareness can hinder
this process. Flexible autonomy aims at trading-off these effects by changing
the level of autonomy within the interaction when required; with
mixed-initiatives combining human preferences and automation's recommendations
to select an appropriate level of autonomy at a certain point of time. However,
the effective implementation of mixed-initiative systems raises fundamental
questions on how to combine human preferences and automation recommendations,
how to realise the selected level of autonomy, and what the future impacts on
the cognitive states of a human are. We explore open challenges that hamper the
process of developing effective flexible autonomy. We then highlight the
potential benefits of using system modelling techniques in HSI by illustrating
how they provide HSI designers with an opportunity to evaluate different
strategies for assessing the state of the mission and for adapting the level of
autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling
Conference, Canberra, Australi
An assigned responsibility system for robotic teleoperation control
This paper proposes an architecture that explores a gap in the spectrum of existing strategies for robot control mode switching in adjustable autonomy. In situations where the environment is reasonably known and/or predictable, pre-planning these control changes could relieve robot operators of the additional task of deciding when and how to switch. Such a strategy provides a clear division of labour between the automation and the human operator(s) before the job even begins, allowing for individual responsibilities to be known ahead of time, limiting confusion and allowing rest breaks to be planned. Assigned Responsibility is a new form of adjustable autonomy-based teleoperation that allows the selective inclusion of automated control elements at key stages of a robot operation plan’s execution. Progression through these stages is controlled by automatic goal accomplishment tracking. An implementation is evaluated through engineering tests and a usability study, demonstrating the viability of this approach and offering insight into its potential applications
On quantifying the value of simulation for training and evaluating robotic agents
Un problème récurrent dans le domaine de la robotique est la difficulté à reproduire les résultats et valider les affirmations faites par les scientifiques. Les expériences conduites en laboratoire donnent fréquemment des résultats propres à l'environnement dans lequel elles ont été effectuées, rendant la tâche de les reproduire et de les valider ardues et coûteuses. Pour cette raison, il est difficile de comparer la performance et la robustesse de différents contrôleurs robotiques. Les environnements substituts à faibles coûts sont populaires, mais introduisent une réduction de performance lorsque l'environnement cible est enfin utilisé. Ce mémoire présente nos travaux sur l'amélioration des références et de la comparaison d'algorithmes (``Benchmarking'') en robotique, notamment dans le domaine de la conduite autonome.
Nous présentons une nouvelle platforme, les Autolabs Duckietown, qui permet aux chercheurs d'évaluer des algorithmes de conduite autonome sur des tâches, du matériel et un environnement standardisé à faible coût. La plateforme offre également un environnement virtuel afin d'avoir facilement accès à une quantité illimitée de données annotées. Nous utilisons la plateforme pour analyser les différences entre la simulation et la réalité en ce qui concerne la prédictivité de la simulation ainsi que la qualité des images générées. Nous fournissons deux métriques pour quantifier l'utilité d'une simulation et nous démontrons de quelles façons elles peuvent être utilisées afin d'optimiser un environnement proxy.A common problem in robotics is reproducing results and claims made by researchers. The experiments done in robotics laboratories typically yield results that are specific to a complex setup and difficult or costly to reproduce and validate in other contexts. For this reason, it is arduous to compare the performance and robustness of various robotic controllers. Low-cost reproductions of physical environments are popular but induce a performance reduction when transferred to the target domain. This thesis present the results of our work toward improving benchmarking in robotics, specifically for autonomous driving.
We build a new platform, the Duckietown Autolabs, which allow researchers to evaluate autonomous driving algorithms in a standardized framework on low-cost hardware. The platform offers a simulated environment for easy access to annotated data and parallel evaluation of driving solutions in customizable environments. We use the platform to analyze the discrepancy between simulation and reality in the case of predictivity and quality of data generated. We supply two metrics to quantify the usefulness of a simulation and demonstrate how they can be used to optimize the value of a proxy environment
Systematic mapping literature review of mobile robotics competitions
This paper presents a systematic mapping literature review about the mobile robotics
competitions that took place over the last few decades in order to obtain an overview of the main
objectives, target public, challenges, technologies used and final application area to show how these
competitions have been contributing to education. In the review we found 673 papers from 5 different
databases and at the end of the process, 75 papers were classified to extract all the relevant information
using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method.
More than 50 mobile robotics competitions were found and it was possible to analyze most of the
competitions in detail in order to answer the research questions, finding the main goals, target public,
challenges, technologies and application area, mainly in education.info:eu-repo/semantics/publishedVersio
AVstack: An Open-Source, Reconfigurable Platform for Autonomous Vehicle Development
Pioneers of autonomous vehicles (AVs) promised to revolutionize the driving
experience and driving safety. However, milestones in AVs have materialized
slower than forecast. Two culprits are (1) the lack of verifiability of
proposed state-of-the-art AV components, and (2) stagnation of pursuing
next-level evaluations, e.g., vehicle-to-infrastructure (V2I) and multi-agent
collaboration. In part, progress has been hampered by: the large volume of
software in AVs, the multiple disparate conventions, the difficulty of testing
across datasets and simulators, and the inflexibility of state-of-the-art AV
components. To address these challenges, we present AVstack, an open-source,
reconfigurable software platform for AV design, implementation, test, and
analysis. AVstack solves the validation problem by enabling first-of-a-kind
trade studies on datasets and physics-based simulators. AVstack solves the
stagnation problem as a reconfigurable AV platform built on dozens of
open-source AV components in a high-level programming language. We demonstrate
the power of AVstack through longitudinal testing across multiple benchmark
datasets and V2I-collaboration case studies that explore trade-offs of
designing multi-sensor, multi-agent algorithms
Keeping Autonomous Driving Alive: An Ethnography of Visions, Masculinity and Fragility
In 'Keeping autonomous driving alive', the author studies the relationships between researchers and artefacts held together by contested visions. Drawing on ethnographic fieldwork in a pioneering research project in Germany, he argues we can make sense of technological visions only if we simultaneously grasp the role of care, gender, and narrative in sustaining technological research. Instead of focusing on the genesis and expansion of sociotechnical assemblages, the book offers a radically new alternative to the study of visions. Building on literature from Science & Technology Studies, Science Communication, and Gender Studies, Göde Both investigates the ambivalence and fragility of technological visions, video demonstrations, and street trials in the hands of researchers invested in self-driving cars. Keeping autonomous driving alive will be of interest to sociologists and anthropologists of technology, gender, and mobility. It is essential reading for those concerned with uncertainty in technological research and with conflicting demands in communicating science. The book provides scholars within the fields of robotics, artificial intelligence, and automotive engineering a means of reflecting on their involvement in self-driving cars. Keeping autonomous driving alive offers science, technology, mobility, and automotive journalists a unique perspective on the present realities of a futuristic technology
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