Autonomy Infused Teleoperation with Application to BCI Manipulation

Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain-Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operator's capabilities and feelings of comfort and control while compensating for a task's difficulty. We present experimental results demonstrating significant performance improvement using the shared-control assistance framework on adapted rehabilitation benchmarks with two subjects implanted with intracortical brain-computer interfaces controlling a seven degree-of-freedom robotic manipulator as a prosthetic. Our results further indicate that shared assistance mitigates perceived user difficulty and even enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with novel objects in densely cluttered environments

Human-guided Swarms: Impedance Control-inspired Influence in Virtual Reality Environments

Prior works in human-swarm interaction (HSI) have sought to guide swarm behavior towards established objectives, but may be unable to handle specific scenarios that require finer human supervision, variable autonomy, or application to large-scale swarms. In this paper, we present an approach that enables human supervisors to tune the level of swarm control, and guide a large swarm using an assistive control mechanism that does not significantly restrict emergent swarm behaviors. We develop this approach in a virtual reality (VR) environment, using the HTC Vive and Unreal Engine 4 with AirSim plugin. The novel combination of an impedance control-inspired influence mechanism and a VR test bed enables and facilitates the rapid design and test iterations to examine trade-offs between swarming behavior and macroscopic-scale human influence, while circumventing flight duration limitations associated with battery-powered small unmanned aerial system (sUAS) systems. The impedance control-inspired mechanism was tested by a human supervisor to guide a virtual swarm consisting of 16 sUAS agents. Each test involved moving the swarm's center of mass through narrow canyons, which were not feasible for a swarm to traverse autonomously. Results demonstrate that integration of the influence mechanism enabled the successful manipulation of the macro-scale behavior of the swarm towards task completion, while maintaining the innate swarming behavior.Comment: 11 pages, 5 figures, preprin

Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction

Autonomy is a critical construct related to human-robot interaction (HRI) and varies widely across robot platforms. Levels of robot autonomy (LORA), ranging from teleoperation to fully autonomous systems, influence the way in which humans and robots interact with one another. Thus, there is a need to understand HRI by identifying variables that influence—and are influenced by—robot autonomy. Our overarching goal is to develop a framework for LORA in HRI. To reach this goal, our framework draws links between HRI and human-automation interaction, a field with a long history of studying and understanding human-related variables. The construct of autonomy is reviewed and redefined within the context of HRI. Additionally, this framework proposes a process for determining a robot’s autonomy level by categorizing autonomy along a 10-point taxonomy. The framework is intended to be treated as a guideline for determining autonomy, categorizing the LORA along a qualitative taxonomy and considering HRI variables (e.g., acceptance, situation awareness, reliability) that may be influenced by the LOR

Hybrid Shared-Autonomy Architecture for Robot Teleoperation with Wearable Interface

Con la diffusione di sistemi robotici è aumentata la necessitàdi avere un controllo efficace da parte degli utenti, ottenuto tramite la condivisione tra utente e robot. In questo lavoro un sistema del genere di shared autonomy in grado di assistere l'utente in un task di presa è migliorato con l'introduzione di collision avoidance, collegando insieme i due attraverso l'archittetura ibrida proposta, la quale viene poi testata e i risultati riportati confermando l'efficacia

Human factors of semi-autonomous robots for urban search and rescue

During major disasters or other emergencies, Urban Search and Rescue (USAR) teams are responsible for extricating casualties safely from collapsed urban structures. The rescue work is dangerous due to possible further collapse, fire, dust or electricity hazards. Sometimes the necessary precautions and checks can last several hours before rescuers are safe to start the search for survivors. Remote controlled rescue robots provide the opportunity to support human rescuers to search the site for trapped casualties while they remain in a safe place. The research reported in this thesis aimed to understand how robot behaviour and interface design can be applied to utilise the benefits of robot autonomy and how to inform future human-robot collaborative systems. The data was analysed in the context of USAR missions when using semi-autonomous remote controlled robot systems. The research focussed on the influence of robot feedback, robot reliability, task complexity, and transparency. The influence of these factors on trust, workload, and performance was examined. The overall goal of the research was to make the life of rescuers safer and enhance their performance to help others in distress. Data obtained from the studies conducted for this thesis showed that semi-autonomous robot reliability is still the most dominant factor influencing trust, workload, and team performance. A robot with explanatory feedback was perceived as more competent, more efficient and less malfunctioning. The explanatory feedback was perceived as a clearer type of communication compared to concise robot feedback. Higher levels of robot transparency were perceived as more trustworthy. However, single items on the trust questionnaire were manipulated and further investigation is necessary. However, neither explanatory feedback from the robot nor robot transparency, increased team performance or mediated workload levels. Task complexity mainly influenced human-robot team performance and the participants’ control allocation strategy. Participants allowed the robot to find more targets and missed more robot errors in the high complexity conditions compared to the low task complexity conditions. Participants found more targets manually in the low complexity tasks. In addition, the research showed that recording the observed robot performance (the performance of the robot that was witnessed by the participant) can help to identify the cause of contradicting results: participants might not have noticed some of the robots mistakes and therefore they were not able to distinguish between the robot reliability levels. Furthermore, the research provided a foundation of knowledge regarding the real world application of USAR in the United Kingdom. This included collecting knowledge via an autoethnographic approach about working processes, command structures, currently used technical equipment, and attitudes of rescuers towards robots. Also, recommendations about robot behaviour and interface design were collected throughout the research. However, recommendations made in the thesis include consideration of the overall outcome (mission performance) and the perceived usefulness of the system in order to support the uptake of the technology in real world applications. In addition, autonomous features might not be appropriate in all USAR applications. When semi-autonomous robot trials were compared to entirely manual operation, only the robot with an average of 97% reliability significantly increased the team performance and reduced the time needed to complete the USAR scenario compared to the manually operated robot. Unfortunately, such high robot success levels do not exist to date. This research has contributed to our understanding of the factors influencing human-robot collaboration in USAR operations, and provided guidance for the next generation of autonomous robots