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.

This paper presents a framework that uses language instructions to define the constraints and objectives for robots gathering information about their environment. Designing autonomous robotic sampling missions requires deep knowledge of both autonomy systems and scientific domain expertise. Language commands provide an intuitive interface for operators to give complex instructions to robots. The key insight we leverage is using topological constraints to define routing directions from the language instruction such as ‘route to the left of the island.’ This work introduces three main contributions: a framework to map language instructions to constraints and rewards for robot planners, a topology constrained information gathering algorithm, and an automatic semantic feature detection algorithm for upwelling fronts. Our work improves on existing methods by not requiring training data with language instruction to planner constraint pairs, allowing new robotic domains such as marine robotics to use our method. This paper provides results demonstrating our framework producing correct constraints for 84.6% of instructions, from a systematically generated corpus of over 1.1 million instructions We also demonstrate the framework producing robot plans from language instructions for real-world scientific sampling missions with the Slocum underwater glider.