Training augmentation using additive sensory noise in a lunar rover navigation task.

Background: The uncertain environments of future space missions means that astronauts will need to acquire new skills rapidly; thus, a non-invasive method to enhance learning of complex tasks is desirable. Stochastic resonance (SR) is a phenomenon where adding noise improves the throughput of a weak signal. SR has been shown to improve perception and cognitive performance in certain individuals. However, the learning of operational tasks and behavioral health effects of repeated noise exposure aimed to elicit SR are unknown. Objective: We evaluated the long-term impacts and acceptability of repeated auditory white noise (AWN) and/or noisy galvanic vestibular stimulation (nGVS) on operational learning and behavioral health. Methods: Subjects (n = 24) participated in a time longitudinal experiment to access learning and behavioral health. Subjects were assigned to one of our four treatments: sham, AWN (55 dB SPL), nGVS (0.5 mA), and their combination to create a multi-modal SR (MMSR) condition. To assess the effects of additive noise on learning, these treatments were administered continuously during a lunar rover simulation in virtual reality. To assess behavioral health, subjects completed daily, subjective questionnaires related to their mood, sleep, stress, and their perceived acceptance of noise stimulation. Results: We found that subjects learned the lunar rover task over time, as shown by significantly lower power required for the rover to complete traverses (p < 0.005) and increased object identification accuracy in the environment (p = 0.05), but this was not influenced by additive SR noise (p = 0.58). We found no influence of noise on mood or stress following stimulation (p > 0.09). We found marginally significant longitudinal effects of noise on behavioral health (p = 0.06) as measured by strain and sleep. We found slight differences in stimulation acceptability between treatment groups, and notably nGVS was found to be more distracting than sham (p = 0.006). Conclusion: Our results suggest that repeatedly administering sensory noise does not improve long-term operational learning performance or affect behavioral health. We also find that repetitive noise administration is acceptable in this context. While additive noise does not improve performance in this paradigm, if it were used for other contexts, it appears acceptable without negative longitudinal effects.

Event-triggered robot self-assessment to aid in autonomy adjustment.

Introduction: Human-robot teams are being called upon to accomplish increasingly complex tasks. During execution, the robot may operate at different levels of autonomy (LOAs), ranging from full robotic autonomy to full human control. For any number of reasons, such as changes in the robot's surroundings due to the complexities of operating in dynamic and uncertain environments, degradation and damage to the robot platform, or changes in tasking, adjusting the LOA during operations may be necessary to achieve desired mission outcomes. Thus, a critical challenge is understanding when and how the autonomy should be adjusted. Methods: We frame this problem with respect to the robot's capabilities and limitations, known as robot competency. With this framing, a robot could be granted a level of autonomy in line with its ability to operate with a high degree of competence. First, we propose a Model Quality Assessment metric, which indicates how (un)expected an autonomous robot's observations are compared to its model predictions. Next, we present an Event-Triggered Generalized Outcome Assessment (ET-GOA) algorithm that uses changes in the Model Quality Assessment above a threshold to selectively execute and report a high-level assessment of the robot's competency. We validated the Model Quality Assessment metric and the ET-GOA algorithm in both simulated and live robot navigation scenarios. Results: Our experiments found that the Model Quality Assessment was able to respond to unexpected observations. Additionally, our validation of the full ET-GOA algorithm explored how the computational cost and accuracy of the algorithm was impacted across several Model Quality triggering thresholds and with differing amounts of state perturbations. Discussion: Our experimental results combined with a human-in-the-loop demonstration show that Event-Triggered Generalized Outcome Assessment algorithm can facilitate informed autonomy-adjustment decisions based on a robot's task competency.

Designing Expandable-Structure Robots for Human-Robot Interaction.

In this paper, we survey the emerging design space of expandable structures in robotics, with a focus on how such structures may improve human-robot interactions. We detail various implementation considerations for researchers seeking to integrate such structures in their own work and describe how expandable structures may lead to novel forms of interaction for a variety of different robots and applications, including structures that enable robots to alter their form to augment or gain entirely new capabilities, such as enhancing manipulation or navigation, structures that improve robot safety, structures that enable new forms of communication, and structures for robot swarms that enable the swarm to change shape both individually and collectively. To illustrate how these considerations may be operationalized, we also present three case studies from our own research in expandable structure robots, sharing our design process and our findings regarding how such structures enable robots to produce novel behaviors that may capture human attention, convey information, mimic emotion, and provide new types of dynamic affordances.