Motivated by challenges operators face with manual control tasks, including fatigue and workload management, this research explores the adoption of a semi-autonomous control method to improve work environment quality and task metrics in controlled situations. Building upon the success of Programming by Demonstration (PbD) for single-arm industrial robotic applications, we extend these techniques to dual-arm robotic control. We present a semi-autonomous approach allowing users to supervise tasks while delegating control to the system, alleviating stress and fatigue associated with manual control operations. This research compares manual and semi-autonomous control in a human-robot team, focusing quantitatively on user performance and qualitatively on trust in the system. Participants controlled a dual-arm robotic system from a remote cockpit, monitoring progress through a graphical user interface (GUI) and camera views. Semi-autonomous control employs PbD with selectable ’motion primitives’. Trials involved a modified pick-and-place task and results demonstrate a significantly higher success rate across all metrics with semi-autonomous control. This study highlights the applicability of PbD as a semi-autonomous control method in human-robot teams, reducing workload stress and enhancing task performance. Integrating sensors for dynamic environment analysis to create motion feedback mechanisms could further enhance user trust and system adaptability. Ultimately, this research suggests implementing semi-autonomous control for dual-arm robotic systems, offering faster onboarding for new operators and increased operational flexibility while minimizing user stress and fatigue.

abstract: Humans and robots need to work together as a team to accomplish certain shared goals due to the limitations of current robot capabilities. Human assistance is required to accomplish the tasks as human capabilities are often better suited for certain tasks and they complement robot capabilities in many situations. Given the necessity of human-robot teams, it has been long assumed that for the robotic agent to be an effective team member, it must be equipped with automated planning technologies that helps in achieving the goals that have been delegated to it by their human teammates as well as in deducing its own goal to proactively support its human counterpart by inferring their goals. However there has not been any systematic evaluation on the accuracy of this claim. In my thesis, I perform human factors analysis on effectiveness of such automated planning technologies for remote human-robot teaming. In the first part of my study, I perform an investigation on effectiveness of automated planning in remote human-robot teaming scenarios. In the second part of my study, I perform an investigation on effectiveness of a proactive robot assistant in remote human-robot teaming scenarios. Both investigations are conducted in a simulated urban search and rescue (USAR) scenario where the human-robot teams are deployed during early phases of an emergency response to explore all areas of the disaster scene. I evaluate through both the studies, how effective is automated planning technology in helping the human-robot teams move closer to human-human teams. I utilize both objective measures (like accuracy and time spent on primary and secondary tasks, Robot Attention Demand, etc.) and a set of subjective Likert-scale questions (on situation awareness, immediacy etc.) to investigate the trade-offs between different types of remote human-robot teams. The results from both the studies seem to suggest that intelligent robots with automated planning capability and proactive support ability is welcomed in general. / Dissertation/Thesis / Masters Thesis Computer Science 2015

Autonomous robots have proven to be useful for search and rescue (SAR) by being deployed in emergency situations and removing the need for direct human presence. However, there remains a need for effective communication between the robot and human operator to cooperate as a team. This thesis investigates the question of when to explain an autonomous agent’s behaviour in the setting of human-robot teaming. A game environment is developed to conduct a virtual SAR experiment to test the effect of explanation timings between two conditions: (1) always explaining - the robot provides explanations whenever possible, and (2) anticipatory explaining - the robot determines when to explain based on the context. When to explain is determined through the construction of argumentation frameworks modelling the context. The effect of anticipatory information pushing is tested on two metrics: team performance and explanation experience. Results indicate anticipatory explaining does not have a significant effect on team performance and participants’ explanation satisfaction. Additionally, participant feedback shows they prefer to be in control instead of cooperating as a team. These findings underline the importance of studying explanation presentation in high-demanding environments and indicate a need for interdisciplinary discussion on the design of human-robot teaming.