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1. Operation and Imitation under Safety-Aware Shared Control

   Broad, Alexander; Murphey, Todd; Argall, Brenna (December 2018, Workshop on the Algorithmic Foundations of Robotics)

   We describe a shared control methodology that can, without knowledge of the task, be used to improve a human’s control of a dynamic system, be used as a training mechanism, and be used in con- junction with Imitation Learning to generate autonomous policies that recreate novel behaviors. Our algorithm introduces autonomy that assists the human partner by enforcing safety and stability constraints. The autonomous agent has no a priori knowledge of the desired task and therefore only adds control information when there is concern for the safety of the system. We evaluate the efficacy of our approach with a human subjects study consisting of 20 participants. We find that our shared control algorithm significantly improves the rate at which users are able to successfully execute novel behaviors. Experimental results suggest that the benefits of our safety-aware shared control algorithm also extend to the human partner’s understanding of the system and their control skill. Finally, we demonstrate how a combination of our safety-aware shared control algorithm and Imitation Learning can be used to autonomously recreate the demonstrated behaviors. 

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2. Exploiting Task Tolerances in Mimicry-based Telemanipulation

   https://doi.org/10.2172/1968187  

   Wang, Yeping; Sifferman, Carter; Gleicher, Michael (October 2023, The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023))

   We explore task tolerances, i.e., allowable position or rotation inaccuracy, as an important resource to facilitate smooth and effective telemanipulation. Task tolerances provide a robot flexibility to generate smooth and feasible motions; however, in teleoperation, this flexibility may make the user’s control less direct. In this work, we implemented a telema- nipulation system that allows a robot to autonomously adjust its configuration within task tolerances. We conducted a user study comparing a telemanipulation paradigm that exploits task tolerances (functional mimicry) to a paradigm that requires the robot to exactly mimic its human operator (exact mimicry), and assess how the choice in paradigm shapes user experience and task performance. Our results show that autonomous adjustments within task tolerances can lead to performance improvements without sacrificing perceived control of the robot. Additionally, we find that users perceive the robot to be more under control, predictable, fluent, and trustworthy in functional mimicry than in exact mimicry. 

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3. Gaze Complements Control Input for Goal Prediction During Assisted Teleoperation

   Aronson, Reuben M.; Admoni, Henny (January 2022, Robotics science and systems)

   Shared control systems can make complex robot teleoperation tasks easier for users. These systems predict the user’s goal, determine the motion required for the robot to reach that goal, and combine that motion with the user’s input. Goal prediction is generally based on the user’s control input (e.g., the joystick signal). In this paper, we show that this prediction method is especially effective when users follow standard noisily optimal behavior models. In tasks with input constraints like modal control, however, this effectiveness no longer holds, so additional sources for goal prediction can improve assistance. We implement a novel shared control system that combines natural eye gaze with joystick input to predict people’s goals online, and we evaluate our system in a real-world, COVID-safe user study. We find that modal control reduces the efficiency of assistance according to our model, and when gaze provides a prediction earlier in the task, the system’s performance improves. However, gaze on its own is unreliable and assistance using only gaze performs poorly. We conclude that control input and natural gaze serve different and complementary roles in goal prediction, and using them together leads to improved assistance. 

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4. Hazard analysis for human-on-the-loop interactions in sUAS systems

   https://doi.org/10.1145/3468264.3468534  

   Vierhauser, Michael; Islam, Md Nafee; Agrawal, Ankit; Cleland-Huang, Jane; Mason, James (August 2021, Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering)

   null (Ed.)

   With the rise of new AI technologies, autonomous systems are moving towards a paradigm in which increasing levels of responsibility are shifted from the human to the system, creating a transition from human-in-the-loop systems to human-on-the-loop (HoTL) systems. This has a significant impact on the safety analysis of such systems, as new types of errors occurring at the boundaries of human-machine interactions need to be taken into consideration. Traditional safety analysis typically focuses on system-level hazards with little focus on user-related or user-induced hazards that can cause critical system failures. To address this issue, we construct domain-level safety analysis assets for sUAS (small unmanned aerial systems) applications and describe the process we followed to explicitly, and systematically identify Human Interaction Points (HiPs), Hazard Factors and Mitigations from system hazards. We evaluate our approach by first investigating the extent to which recent sUAS incidents are covered by our hazard trees, and second by performing a study with six domain experts using our hazard trees to identify and document hazards for sUAS usage scenarios. Our study showed that our hazard trees provided effective coverage for a wide variety of sUAS application scenarios and were useful for stimulating safety thinking and helping users to identify and potentially mitigate human-interaction hazards. 

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5. Measuring Human-Robot Team Benefits Under Time Pressure in a Virtual Reality Testbed

   Katarina Popovic, Millicent Schlafly (October 2023, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   During a natural disaster such as hurricane, earth- quake, or fire, robots have the potential to explore vast areas and provide valuable aid in search & rescue efforts. These scenar- ios are often high-pressure and time-critical with dynamically- changing task goals. One limitation to these large scale deploy- ments is effective human-robot interaction. Prior work shows that collaboration between one human and one robot benefits from shared control. Here we evaluate the efficacy of shared control for human-swarm teaming in an immersive virtual reality environment. Although there are many human-swarm interaction paradigms, few are evaluated in high-pressure settings representative of their intended end use. We have developed an open-source virtual reality testbed for realistic evaluation of human-swarm teaming performance under pressure. We conduct a user study (n=16) comparing four human-swarm paradigms to a baseline condition with no robotic assistance. Shared control significantly reduces the number of instructions needed to operate the robots. While shared control leads to marginally improved team performance in experienced participants, novices perform best when the robots are fully autonomous. Our experimental results suggest that in immersive, high-pressure settings, the benefits of robotic assistance may depend on how the human and robots interact and the human operator’s expertise. 

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