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

On Mixed-Initative Planning and Control for Autonomous Underwater Vehicles

Supervision and control of Autonomous underwater vehicles (AUVs) has traditionally been focused on an operator determining a priori the sequence of waypoints of a single vehicle for a mission. As AUVs become more ubiquitous as a scientific tool, we envision the need for controlling multiple vehicles which would impose less cognitive burden on the operator with a more abstract form of human-in-the-loop control. Such mixed-initiative methods in goal-oriented commanding are new for the oceanographic domain and we describe the motivations and preliminary experiments with multiple vehicles operating simultaneously in the water, using a shore-based automated planner

A systems approach to evaluate One Health initiatives

Challenges calling for integrated approaches to health, such as the One Health (OH) approach, typically arise from the intertwined spheres of humans, animals, and ecosystems constituting their environment. Initiatives addressing such wicked problems commonly consist of complex structures and dynamics. As a result of the EU COST Action (TD 1404) “Network for Evaluation of One Health” (NEOH), we propose an evaluation framework anchored in systems theory to address the intrinsic complexity of OH initiatives and regard them as subsystems of the context within which they operate. Typically, they intend to influence a system with a view to improve human, animal, and environmental health. The NEOH evaluation framework consists of four overarching elements, namely: (1) the definition of the initiative and its context, (2) the description of the theory of change with an assessment of expected and unexpected outcomes, (3) the process evaluation of operational and supporting infrastructures (the “OH-ness”), and (4) an assessment of the association(s) between the process evaluation and the outcomes produced. It relies on a mixed methods approach by combining a descriptive and qualitative assessment with a semi-quantitative scoring for the evaluation of the degree and structural balance of “OH-ness” (summarised in an OH-index and OH-ratio, respectively) and conventional metrics for different outcomes in a multi-criteria-decision-analysis. Here, we focus on the methodology for Elements (1) and (3) including ready-to-use Microsoft Excel spreadsheets for the assessment of the “OH-ness”. We also provide an overview of Element (2), and refer to the NEOH handbook for further details, also regarding Element (4) (http://neoh.onehealthglobal.net). The presented approach helps researchers, practitioners, and evaluators to conceptualise and conduct evaluations of integrated approaches to health and facilitates comparison and learning across different OH activities thereby facilitating decisions on resource allocation. The application of the framework has been described in eight case studies in the same Frontiers research topic and provides first data on OH-index and OH-ratio, which is an important step towards their validation and the creation of a dataset for future benchmarking, and to demonstrate under which circumstances OH initiatives provide added value compared to disciplinary or conventional health initiatives

Reactive task planning for multi-robot systems in partial known environment

openThe thesis investigates the planning and control problem for a group of mobile agents moving in a partially known workspace. A task will be assigned to each robot in the form of a linear temporal logic (LTL) formula. First an automaton-based method is introduced for the motion planning of a single agent, which guarantees the satisfaction of the assigned LTL task. Then a model-predictive controller considers state and input constraints leading the agent to a safe navigation. Based on a real scenario of a partial-known environment and agents can have only local sensing, two decentralized control strategies are proposed for online re-planning, which rely on a sampling-based algorithm. The first approach assumes local communication between agents, while the second one exploits a more general communication-free case. Finally, the human-in-the-loop scenario is considered, where a human may additionally take control of the agents, a mixed initiative controller is then implemented to prevent dangerous human behaviors while guarantee the satisfaction of the LTL specification. Using the developed ROS software package, several experiments were carried out to demonstrate the effectiveness and the potential applicability of the proposed strategies.The thesis investigates the planning and control problem for a group of mobile agents moving in a partially known workspace. A task will be assigned to each robot in the form of a linear temporal logic (LTL) formula. First an automaton-based method is introduced for the motion planning of a single agent, which guarantees the satisfaction of the assigned LTL task. Then a model-predictive controller considers state and input constraints leading the agent to a safe navigation. Based on a real scenario of a partial-known environment and agents can have only local sensing, two decentralized control strategies are proposed for online re-planning, which rely on a sampling-based algorithm. The first approach assumes local communication between agents, while the second one exploits a more general communication-free case. Finally, the human-in-the-loop scenario is considered, where a human may additionally take control of the agents, a mixed initiative controller is then implemented to prevent dangerous human behaviors while guarantee the satisfaction of the LTL specification. Using the developed ROS software package, several experiments were carried out to demonstrate the effectiveness and the potential applicability of the proposed strategies

Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time

This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions. We implement two components of the Cycle-of-Learning for Autonomous Systems, which is our framework for combining multiple modalities of human interaction. The current effort employs human demonstrations to teach a desired behavior via imitation learning, then leverages intervention data to correct for undesired behaviors produced by the imitation learner to teach novel tasks to an autonomous agent safely, after only minutes of training. We demonstrate this method in an autonomous perching task using a quadrotor with continuous roll, pitch, yaw, and throttle commands and imagery captured from a downward-facing camera in a high-fidelity simulated environment. Our method improves task completion performance for the same amount of human interaction when compared to learning from demonstrations alone, while also requiring on average 32% less data to achieve that performance. This provides evidence that combining multiple modes of human interaction can increase both the training speed and overall performance of policies for autonomous systems.Comment: 9 pages, 6 figure