Purpose of Review: The paper discusses how robotics and autonomous systems (RAS) are being deployed to decarbonise agricultural production. The climate emergency cannot be ameliorated without dramatic reductions in greenhouse gas emissions across the agri-food sector. This review outlines the transformational role for robotics in the agri-food system and considers where research and focus might be prioritised. Recent Findings: Agri-robotic systems provide multiple emerging opportunities that facilitate the transition towards net zero agriculture. Five focus themes were identified where robotics could impact sustainable food production systems to (1) increase nitrogen use efficiency, (2) accelerate plant breeding, (3) deliver regenerative agriculture, (4) electrify robotic vehicles, (5) reduce food waste. Summary: RAS technologies create opportunities to (i) optimise the use of inputs such as fertiliser, seeds, and fuel/energy; (ii) reduce the environmental impact on soil and other natural resources; (iii) improve the efficiency and precision of agricultural processes and equipment; (iv) enhance farmers’ decisions to improve crop care and reduce farm waste. Further and scaled research and technology development are needed to exploit these opportunities.

Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out cross-sectional and longitudinal analyses to show topic distributions and topic dynamics throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification and identify several patterns and trajectories. For example, although members discuss mainly disease-related topics, their interest may change through time and vary with their disease severities.

This paper presents the results of a user study in which the impact of sharing decision making in a human-robot team was measured. In the experiments outlined here, a human and robot play a game together in which the robot searches an arena for items, with input from the human, and the human-robot team earns points for finding and correctly identifying the items. The user study reported here involved 60 human subjects. Each subject interacted with two different robots. With one robot, the human acted as a supervisor: the human issued commands and the robot obeyed. With the other robot, the human acted as a collaborator: the human and robot shared decisions and were required to reach agreement about the robot’s actions in the arena before any actions were taken, facilitated using computational argumentation. Objective performance metrics were collected and analyzed for both types of human-robot team, as well subjective feedback from human subjects regarding attitudes toward working with a robot. The objective results showed significant improvement in performance metrics with the human-as-collaborator pairs versus the human-as-supervisor pairs. Subjective results demonstrated significant differences across many subjective measures and indicated a distinct preference for the human-as-collaborator mode. The primary contribution of this work lies in the demonstration and evaluation of a computational argumentation approach to human-robot interaction, particularly in proving the efficacy of this approach over a less autonomous mode of interaction.

Global food security is a critical issue today, strained by a wide range of factors including global warming, carbon emissions, sociopolitical and economic challenges, traditional workforce decline and population growth. Technical innovations that address food security, like agricultural robotics, are gaining traction in industry settings, moving from controlled labs and experimental test facilities to real-world environments. Such technologies require sufficient network infrastructure to support in-field operations; thus, there is increased urgency to establish reliable, high-speed wireless communication networking solutions that enable deployment of autonomous agri-robots. The work presented here includes two contributions at the intersection of network infrastructure and in-field agricultural robotics. First, the physical performance of a private 5G-SA system in an agri-robotics application is evaluated and in-field experimental results are presented. These results are compared (using the same experimental setup) against public 4G and private WiFi6 (a newly emerging wireless communication standard). Second, a simulated experiment was performed to assess the “real-time” operational delay in critical tasks that may require quick turnaround between in-field robot and off-board processing. The results demonstrate that public 4G cannot be used in the agricultural domain for applications that require high throughput and reliable communication; that private 5G-SA greatly outperforms public 4G in all performance metrics (as expected); and that private WiFi6, though limited in range, is a fast and very reliable alternative in specific settings. While a single wireless solution does not currently exist for the agricultural domain, multiple technologies can be combined in a hybrid solution that meets the communications requirements.

Dialogue can support exchange of ideas and discussion of options as a means to enable shared decision making for human-robot collaboration. However, dialogue that supports dynamic, evidence-backed exchange of ideas is a major challenge for today's human-robot systems. The work presented here investigates the application of argumentation-based dialogue games as the means to facilitate flexible interaction, including unscripted changes in initiative. Two main contributions are provided in this paper. First, a methodology for implementing multiple types of argumentation-based dialogues for human-robot interaction is detailed. This includes explanation about which types of dialogues are appropriate given the beliefs of the participants and how multiple dialogues can occur simultaneously while maintaining a consistent set of beliefs for the participants. Second, a formal definition is presented for the Treasure Hunt Game (THG), a test environment that provides rich opportunities for experimentation in shared human-robot control, as well as motivating and engaging experiences for human subjects.