In this paper, we investigate the impact of high-level semantics (evaluation of the environment) on Human-Robot Team (HRT) and Human-Robot Interaction (HRI) in the context of mobile robot deployments. Although semantics has been widely researched in AI, how high-level semantics can benefit the HRT paradigm is underexplored, often fuzzy, and intractable. We applied a semantics-based framework that could reveal different indicators of the environment (i.e. how much semantic information exists) in a mock-up disaster response mission. In such missions, semantics are crucial as the HRT should handle complex situations and respond quickly with correct decisions, where humans might have a high workload. Especially when human operators need to shift their attention between robots and other tasks, they will struggle to build Situational Awareness (SA) quickly. The experiment suggests that the presented semantics: 1) alleviate the perceived human operator’s workload; 2) increase the operator’s trust in the SA; and 3) help to reduce the reaction time in switching the Level of Autonomy (LoA) when needed. Additionally, we find that participants with higher trust in the system are encouraged by high-level semantics to use teleoperation mode more.

This paper presents a novel method to quantify Trust in HRI. It proposes an HRI framework for estimating the Robot Trust towards the Human in the context of a narrow and specified task. The framework produces a real-time estimation of an AI agent's Artificial Trust towards a Human partner interacting with a mobile teleoperation robot. The approach for the framework is based on principles drawn from Theory of Mind, including information about the human state, action, and intent. The framework creates the ATTUNE model for Artificial Trust Towards Human Operators. The model uses metrics on the operator's state of attention, navigational intent, actions, and performance to quantify the Trust towards them. The model is tested on a pre-existing dataset that includes recordings (ROSbags) of a human trial in a simulated disaster response scenario. The performance of ATTUNE is evaluated through a qualitative and quantitative analysis. The results of the analyses provide insight into the next stages of the research and help refine the proposed approach.

Adjusting the level of autonomy in human-machine systems (e.g., human-robot systems) holds great potential for achieving high system performance while maintaining operator involvement. To support operators with the task of setting the proper level of autonomy, we present a novel approach to realise a Model Predictive Controller that determines the optimal LoA for each tessellation in the robot's path plan based on the estimated performance degradation due environmental adversities. We also report on an experimental evaluation of a mixed-initiative system where both the operator and the Model Predictive Controller are in charge of dynamically adjusting the level of autonomy cooperatively while performing a challenging navigational task with a mobile ground robot in a high-fidelity simulation. To this end, we conducted a user study with 15 participants comparing the performance and user experience of the model predictive system with a state-of-the-art system. The results show significant benefits of the model predictive system in terms of a reduction of conflicts for control and an improved user experience. Additionally, there are indications of benefits in terms of robot health and, consequently, performance for the model predictive system.

This paper reports on insights by robotics researchers that participated in a 5-day robot-assisted nuclear disaster response field exercise conducted by Kerntechnische Hilfdienst GmbH (KHG) in Karlsruhe, Germany. The German nuclear industry established KHG to provide a robot-assisted emergency response capability for nuclear accidents. We present a systematic description of the equipment used; the robot operators' training program; the field exercise and robot tasks; and the protocols followed during the exercise. Additionally, we provide insights and suggestions for advancing disaster response robotics based on these observations. Specifically, the main degradation in performance comes from the cognitive and attentional demands on the operator. Furthermore, robotic platforms and modules should aim to be robust and reliable in addition to their ease of use. Last, as emergency response stakeholders are often skeptical about using autonomous systems, we suggest adopting a variable autonomy paradigm to integrate autonomous robotic capabilities with the human-in-the-loop gradually. This middle ground between teleoperation and autonomy can increase end-user acceptance while directly alleviating some of the operator's robot control burden and maintaining the resilience of the human-in-the-loop.

This paper addresses the problem of transfer of control authority between a robot’s AI and a remote human operator, when controlling a Mixed-Initiative (MI) robotic system. We propose a negotiation-theoretic method that enables the robot’s AI and the human operator to cooperatively and dynamically determine (i. e. negotiate) the transfer of control authority between these two agents. An experimental study is presented in which a state-of-the-art Expert-guided Mixed-Initiative Control Switcher (EMICS) method is compared with our proposed Negotiation-Enabled Mixed-Initiative Control Switcher (NEMICS) algorithm. Results suggest that the NEMICS framework is able to successfully avoid conflicts for control, which is a fundamental challenge encountered with previous MI control methods. Comparing NEMICS with the EMICS, we provide evidence of improved navigational safety (i. e. fewer collisions). Additionally, our usability study suggests that human operators perceived their interactions with NEMICS as less intrusive than with EMICS.

This paper investigates how trust, shared under-standing between a human operator and a robot, and the Locus of Control (LoC) personality trait, evolve and affect Human-Robot Interaction (HRI) in mixed-initiative robotic systems. As such systems become more advanced and able to instigate actions alongside human operators, there is a shift from robots being perceived as a tool to being a team-mate. Hence, the team-oriented human factors investigated in this paper (i.e. trust, shared understanding, and LoC) can play a crucial role in efficient HRI. Here, we present the results from an experiment inspired by a disaster response scenario in which operators remotely controlled a mobile robot in navigation tasks, with either human-initiative or mixed-initiative control, switching dynamically between two different levels of autonomy: teleoperation and autonomous navigation. Evidence suggests that operators trusted and developed an understanding of the robotic systems, especially in mixed-initiative control, where trust and understanding increased over time, as operators became more familiar with the system and more capable of performing the task. Lastly, evidence and insights are presented on how LoC affects HRI.

This paper addresses the problem of the human operator cognitive workload estimation while controlling a robot. Being capable of assessing, in real-time, the operator’s workload could help prevent calamitous events from occurring. This workload estimation could enable an AI to make informed decisions to assist or advise the operator, in an advanced human-robot interaction framework. We propose a method, named Fessonia, for real-time cognitive workload estimation from multiple parameters of an operator’s driving behaviour via the use of behavioural entropy. Fessonia is comprised of: a method to calculate the entropy (i.e. unpredictability) of the operator driving behaviour profile; the Driver Profile Update algorithm which adapts the entropy calculations to the evolving driving profile of individual operators; and a Warning And Indication System that uses workload estimations to issue advice to the operator. Fessonia is evaluated in a robot teleoperation scenario that incorporated cognitively demanding secondary tasks to induce varying degrees of workload. The results demonstrate the ability of Fessonia to estimate different levels of imposed workload. Additionally, it is demonstrated that our approach is able to detect and adapt to the evolving driving profile of the different operators. Lastly, based on data obtained, a decrease in entropy is observed when a warning indication is issued, suggesting a more attentive approach focused on the primary navigation task.

This paper addresses the problem of human operator intent recognition during teleoperated robot navigation. In this context, recognition of the operator’s intended navigational goal, could enable an artificial intelligence (AI) agent to assist the operator in an advanced human-robot interaction framework. We propose a Bayesian Operator Intent Recognition (BOIR) probabilistic method that utilizes: (i) an observation model that fuses information as a weighting combination of multiple observation sources providing geometric information; (ii) a transition model that indicates the evolution of the state; and (iii) an action model, the Active Intent Recognition Model (AIRM), that enables the operator to communicate their explicit intent asynchronously. The proposed method is evaluated in an experiment where operators controlling a remote mobile robot are tasked with navigation and exploration under various scenarios with different map and obstacle layouts. Results demonstrate that BOIR outperforms two related methods from literature in terms of accuracy and uncertainty of the intent recognition.

This article presents an Expert-guided Mixed-initiative Control Switcher (EMICS) for remotely operated mobile robots. The EMICS enables switching between different levels of autonomy during task execution initiated by either the human operator and/or the EMICS. The EMICS is evaluated in two disaster-response-inspired experiments, one with a simulated robot and test arena, and one with a real robot in a realistic environment. Analyses from the two experiments provide evidence that: (a) Human-Initiative (HI) systems outperform systems with single modes of operation, such as pure teleoperation, in navigation tasks; (b) in the context of the simulated robot experiment, Mixed-initiative (MI) systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload compared to HI. Last, our experiment on a physical robot provides empirical evidence that identify two major challenges for MI control: (a) the design of context-aware MI control systems; and (b) the conflict for control between the robot’s MI control system and the operator. Insights regarding these challenges are discussed and ways to tackle them are proposed.

This paper investigates learning effects and human operator training practices in variable autonomy robotic systems. These factors are known to affect performance of a human-robot system and are frequently overlooked. We present the results from an experiment inspired by a search and rescue scenario in which operators remotely controlled a mobile robot with either Human-Initiative (HI) or Mixed-Initiative (MI) control. Evidence suggests learning in terms of primary navigation task and secondary (distractor) task performance. Further evidence is provided that MI and HI performance in a pure navigation task is equal. Lastly, guidelines are proposed for experimental design and operator training practices.

This paper presents a principled experimental analysis of a variable autonomy control approach to mobile robot navigation. A Human-Initiative (HI) variable autonomy system is investigated, in which a human operator is able to switch the Level of Autonomy (LOA) between teleoperation (joystick control) and autonomous control (robot navigates autonomously towards waypoints selected by the human) on-the-fly. Our hypothesis is that the HI system will enable superior navigation performance compared to either teleoperation or autonomy alone, especially in scenarios where the performance of both the human and the robot may at times become degraded. We evaluate our hypothesis through carefully controlled and repeatable experiments using a significant number of human test-subjects.

Safety critical and demanding tasks (e.g. Search and rescue or hazardous environments inspection), can benefit from robotic systems that offer a spectrum of control modes. These can range from direct teleoperation to full autonomy. This paper describes a pilot-study experiment in which a variable autonomy robot completes a navigation task. It explores the comparative performances of the human-robot system at different autonomy levels under different sets of conditions. This is done from a Mixed-Initiative system investigation perspective. Sensor noise was added to degrade robot performance, while a secondary task induced varying degrees of additional workload on the human operator. Carrying out these experiments and analyzing the initial results, has highlighted the profound complexities of designing tasks, conditions, and performance metrics which are: principled, eliminate confounding factors, and yield scientifically rigorous insights into the intricacies of a collaborative system that combines both human and robot intelligences. A key contribution of this paper is to describe the lessons learned from attempting these experiments, and to suggest a variety of guidelines for other researchers to consider when designing experiments in this context.