PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection

Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research. However, collecting large-scale robotic data is much more expensive and slower as each operator can control only a single robot at a time. To make this costly data collection process efficient and scalable, we propose Policy Assisted TeleOperation (PATO), a system which automates part of the demonstration collection process using a learned assistive policy. PATO autonomously executes repetitive behaviors in data collection and asks for human input only when it is uncertain about which subtask or behavior to execute. We conduct teleoperation user studies both with a real robot and a simulated robot fleet and demonstrate that our assisted teleoperation system reduces human operators' mental load while improving data collection efficiency. Further, it enables a single operator to control multiple robots in parallel, which is a first step towards scalable robotic data collection. For code and video results, see https://clvrai.com/patoComment: Website: https://clvrai.com/pat

Analysis of autopilot disengagements occurring during autonomous vehicle testing

In present-day highly-automated vehicles, there are occasions when the driving system disengages and the human driver is required to take-over. This is of great importance to a vehicle U+02BC s safety and ride comfort. In the U.S state of California, the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing. On 1 January 2016, seven manufacturers submitted their first disengagement reports: Bosch, Delphi, Google, Nissan, Mercedes-Benz, Volkswagen, and Tesla Motors. This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover, as this is potentially useful in improving the Society of Automotive Engineers U+0028 SAE U+0029 Level 2 and Level 3 automation technologies. Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail. The mechanisms and time taken for take-over transition occurred in disengagements are studied. Finally, recommendations for OEMs, manufacturers, and government organizations are also discussed

Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Improvements in autonomy offer the potential for positive outcomes in a number of domains, yet guaranteeing their safe deployment is difficult. This work investigates how humans can intelligently supervise agents to achieve some level of safety even when performance guarantees are elusive. The motivating research question is: In safety-critical settings, can we avoid the need to have one human supervise one machine at all times? The paper formalizes this 'scaling supervision' problem, and investigates its application to the safety-critical context of autonomous vehicles (AVs) merging into traffic. It proposes a conservative, reachability-based method to reduce the burden on the AVs' human supervisors, which allows for the establishment of high-confidence upper bounds on the supervision requirements in this setting. Order statistics and traffic simulations with deep reinforcement learning show analytically and numerically that teaming of AVs enables supervision time sublinear in AV adoption. A key takeaway is that, despite present imperfections of AVs, supervision becomes more tractable as AVs are deployed en masse. While this work focuses on AVs, the scalable supervision framework is relevant to a broader array of autonomous control challenges.Comment: 14 pages, 7 figure

A Review of Shared Control for Automated Vehicles: Theory and Applications

The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years

A Review of Shared Control for Automated Vehicles: Theory and Applications

The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.This work was supported in part by the ECSEL Joint Undertaking, which funded the PRYSTINE project under Grant 783190, and in part by the AUTOLIB project (ELKARTEK 2019 ref. KK-2019/00035; Gobierno Vasco Dpto. Desarrollo económico e infraestructuras)

A Topology of Shared Control Systems—Finding Common Ground in Diversity

Shared control is an increasingly popular approach to facilitate control and communication between humans and intelligent machines. However, there is little consensus in guidelines for design and evaluation of shared control, or even in a definition of what constitutes shared control. This lack of consensus complicates cross fertilization of shared control research between different application domains. This paper provides a definition for shared control in context with previous definitions, and a set of general axioms for design and evaluation of shared control solutions. The utility of the definition and axioms are demonstrated by applying them to four application domains: automotive, robot-assisted surgery, brain–machine interfaces, and learning. Literature is discussed for each of these four domains in light of the proposed definition and axioms. Finally, to facilitate design choices for other applications, we propose a hierarchical framework for shared control that links the shared control literature with traded control, co-operative control, and other human–automation interaction methods. Future work should reveal the generalizability and utility of the proposed shared control framework in designing useful, safe, and comfortable interaction between humans and intelligent machines

Human-Machine Cooperative Decision Making

Diese Dissertation beschäftigt sich mit der gemeinsamen Entscheidungsfindung in der Mensch-Maschine-Kooperation und liefert neue Erkenntnisse, welche von der theoretischen Modellierung bis zu experimentellen Untersuchungen reichen. Zunächst wird eine methodische Klassifikation bestehender Forschung zur Mensch-Maschine-Kooperation vorgenommen und der Forschungsfokus dieser Dissertation mithilfe eines vorgestellten Taxonomiemodells der Mensch-Maschine-Kooperation, dem Butterfly-Modell, abgegrenzt. Darauffolgend stellt die Dissertation zwei mathematische Verhaltensmodelle der gemeinsamen Entscheidungsfindung von Mensch und Maschine vor: das Adaptive Verhandlungsmodell und den n-stufigen War of Attrition. Beide modellieren den Einigungsprozess zweier emanzipierter Kooperationspartner und unterscheiden sich hinsichtlich ihrer Ursprünge, welche in der Verhandlungs- beziehungsweise Spieltheorie liegen. Zusätzlich wird eine Studie vorgestellt, die die Eignung der vorgeschlagenen mathematischen Modelle zur Beschreibung des menschlichen Nachgebeverhaltens in kooperativen Entscheidungsfindungs-Prozessen nachweist. Darauf aufbauend werden zwei modellbasierte Automationsdesigns bereitgestellt, welche die Entwicklung von Maschinen ermöglichen, die an einem Einigungsprozess mit einem Menschen teilnehmen können. Zuletzt werden zwei experimentelle Untersuchungen der vorgeschlagenen Automationsdesigns im Kontext von teleoperierten mobilen Robotern in Such- und Rettungsszenarien und anhand einer Anwendung in einem hochautomatisierten Fahrzeug präsentiert. Die experimentellen Ergebnisse liefern empirische Evidenz für die Überlegenheit der vorgestellten modellbasierten Automationsdesigns gegenüber den bisherigen Ansätzen in den Aspekten der objektiven kooperativen Performanz, des menschlichen Vertrauens in die Interaktion mit der Maschine und der Nutzerzufriedenheit. So zeigt diese Dissertation, dass Menschen eine emanzipierte Interaktion mit Bezug auf die Entscheidungsfindung bevorzugen, und leistet einen wertvollen Beitrag zur vollumfänglichen Betrachtung und Verwirklichung von Mensch-Maschine-Kooperationen

Human-Machine Cooperative Decision Making

The research reported in this thesis focuses on the decision making aspect of human-machine cooperation and reveals new insights from theoretical modeling to experimental evaluations: Two mathematical behavior models of two emancipated cooperation partners in a cooperative decision making process are introduced. The model-based automation designs are experimentally evaluated and thereby demonstrate their benefits compared to state-of-the-art approaches