We present a field deployment of a collaborative scientist-robot system that enables improved scientific gain for planetary science missions. In this system, the scientist interacts with the autonomy through a computer interface to specify prior disciplinary knowledge, hypothesis information, and refine the mission objectives using preferences and ratings. The goal of this interaction is to use autonomy where it works best, optimizing robot data collection paths under a set of constraints, while enabling scientists to communicate priors and objectives in a straightforward and efficient manner. The system was deployed with a quadruped during a planetary analog field science mission at White Sands National Park, New Mexico, to support the investigation of the surface and shallow subsurface properties of the Martian analog dunes. The system was used to generate robot paths for three different mission scientists, who had varying priors and objectives. A quadruped robot executed the path providing data on the stiffness of the surface measured through ground-leg interactions. The generated dense stiffness measurements were used by scientists to select refined locations for follow-up data collection. While, we found that more work is needed to fully incorporate human priors, our deployed system enabled scientists to collect mission-critical data more efficiently, and they were able to adapt the system to their science needs.

In this article, we investigate using explanations to improve decision-making for robotic scientific data collection missions. We propose the Preference Elicitation with contrasting Feature-based eXplanations (PrEFeX) method, which combines preferences with contrasting explanations focused on a single explanatory feature. We first provide a verification of the contrasting explanations by themselves using a planner prediction user study with 16 expert participants (Phase 1). This study showed that there was no increase in understanding of robot plans using contrasting explanations without preferences. To elucidate what information the autonomous measurement selection system was missing to be useful, we interviewed 4 planetary scientists and 2 oceanographers (Phase 2). We found that scientists focused heavily on understanding the objectives of the system and wanted explanations that (1) grounded the explanations to tradeoffs the system made, (2) connected the explanations to an ability to modify the behavior of the decision making, and (3) attached the explanation system’s features to comparable features the scientists considered. To this end, we propose combining explanations with user preference learning of the reward function in an iterative design process of the measurement plans with scientists in the loop. We tested our proposed preference and explanation system in a field deployment with planetary scientists on Mt. Hood, Oregon, and performed a post-data collection survey on the quality of the plans with 22 experts (Phase 3). We found the experts preferred the measurement plan selected by our proposed PrEFeX method over a baseline without explanations or preferences.

Despite an increased reliance on robotic systems, the field science community remains resistant to adopting information gathering algorithms for generating data collection plans, with most data collection decisions still being made by expert field scientists. This is a missed opportunity, as coordinating low-level data collection decisions is time-intensive, cognitively taxing, and prevents field scientists from being able to handle high-level tasks that they are better suited for. In this paper, we present an initial effort to understand the reasons for the slow algorithm adoption through two web-based experiments. In the first study, expert planetary scientists (N = 82) blindly evaluated three expert-generated data collection plans and an algorithm-generated plan. In a follow-up study, scientists (N = 33) were provided with the data collected using the different plans and asked to update their evaluations. We found scientists perceived algorithm-generated plans as less-than-ideal relative to expert-generated plans and providing scientists the resulting data did not improve their perception of the plans. Scientists identified characteristics of expert-generated plans that were not present in algorithm-generated plans: more even measurement coverage, more measurements transecting gradients of interest, and control measurements that better capture extreme ends of a variable range. Future work should take inspiration from how expert scientists make data collection decisions to improve information gathering algorithms and their uptake for robot-aided field science missions.

Our analysis of human sampling decision data reveals that scientists adapt their sampling strategies to balance multiple objectives based on two key factors: the current level of information about the environment, and the availability of sampling location options with large potential rewards. While this work is only a beginning step towards the development of cognitive-compatible robotic decision algorithms, our findings show by better understanding human decision processes, robots can use extremely simple algorithms to connect experts' high-level objectives to desired sampling locations while balancing multiple objectives. Going forward, exploring how humans coordinate and prioritize multiple objectives under more sophisticated scientific exploration scenarios, such as with multiple competing hypotheses, with hypotheses regarding multiple variables, or with additional sampling objectives, would be helpful to explore. These understandings could help our robots produce explainable sampling strategies that are well-aligned with humans' high level goals, and improve humans' trust and confidence during teaming. These cognitive understandings could also allow robots to identify potential vulnerabilities in human decisions, such as biases and fatigue, and provide targeted support to enhance scientific outcomes. In addition, we expect that these cognitive insights could complement existing robotic decision methods by informing which algorithms to use, and eventually empower robots to become intelligent teammates that can truly participate in the decision-making process.

Devices that deliver deep pressure sensations (DPS) are common, but how well do these systems actually work, and are DPS experiences different across devices? To help address these questions, we previously designed a portable and fast-acting soft-robotic DPS alternative: the AID Vest. In this work, we evaluate the AID Vest’s effect on individuals with moderate or high anxiety specifically. We conducted a study with N = 10 participants, providing experiences with a weighted blanket and the AID Vest, and measuring biosignals, one-shot self-reports, and exploratory continuous self-reports related to these experiences. The results show reductions in established biosignal and self-report methods for measuring anxiety for both DPS experiences. The continuous self-report results were mixed, but may be useful for future hypothesis generation. This work shows more positive AID Vest effects compared to our past work on convenience population users, and our results can inform others with interest in DPS applications such as anxiety management.

In this work, a controlled user study explores four Cartesian spatial trajectories (high, low, direct, and from the side arcs), with three unique objects (flower, dagger, water). In mathematics, the Cartesian planes of horizontal, vertical, and forward/back are widely used for path-planning, much like any animal seeking to traverse the surface of the earth. Gravity naturally pulls objects down, and extra force must be exerted to push them upwards. In Laban notation, a dance annotation language, actors and dancers are similarly taught to think of table, door, and wheel planes (direct Cartesian correlates). What is the impact of these cardinal directions on interpretation of robot handovers? In these results, we find that direct paths, water, and flowers are generally seen positively, with daggers and high paths as least friendly. There is also a statistically significant interaction between path and object on robot predictability ratings. In terms of nuance, we also find that elegance is best predicted by path shape. These findings expand prior features of mobile robot expression to arcing planes, finding the up/down degree of freedom to significantly impact human interpretation and experience. Future work can continue such investigations grounded in real world tasks such as assistive robotics (passing a blanket or tissue), or service (retrieving a ticket for a bus).

Truly collaborative scientific field data collection between human scientists and autonomous robot systems requires a shared understanding of the search objectives and tradeoffs faced when making decisions. Therefore, critical to developing intelligent robots to aid human experts is an understanding of how scientists make such decisions and how they adapt their data collection strategies when presented with new information in situ . In this study, we examined the dynamic data collection decisions of 108 expert geoscience researchers using a simulated field scenario. Human data collection behaviors suggested two distinct objectives: an information-based objective to maximize information coverage and a discrepancy-based objective to maximize hypothesis verification. We developed a highly simplified quantitative decision model that allows the robot to predict potential human data collection locations based on the two observed human data collection objectives. Predictions from the simple model revealed a transition from information-based to discrepancy-based objective as the level of information increased. The findings will allow robotic teammates to connect experts’ dynamic science objectives with the adaptation of their sampling behaviors and, in the long term, enable the development of more cognitively compatible robotic field assistants.