Human-guided robotic exploration is a useful approach to gathering information at remote locations, especially those that might be too risky, inhospitable, or inaccessible for humans. Maintaining common ground between the remotely-located partners is a challenge, one that can be facilitated by multi-modal communication. In this paper, we explore how participants utilized multiple modalities to investigate a remote location with the help of a robotic partner. Participants issued spoken natural language instructions and received from the robot: text-based feedback, continuous 2D LIDAR mapping, and upon-request static photographs. We noticed that different strategies were adopted in terms of use of the modalities, and hypothesize that these differences may be correlated with success at several exploration sub-tasks. We found that requesting photos may have improved the identification and counting of some key entities (doorways in particular) and that this strategy did not hinder the amount of overall area exploration. Future work with larger samples may reveal the effects of more nuanced photo and dialogue strategies, which can inform the training of robotic agents. Additionally, we announce the release of our unique multi-modal corpus of human-robot communication in an exploration context: SCOUT, the Situated Corpus on Understanding Transactions.

Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event schema induction either focuses on atomic events or linear temporal event sequences, ignoring the interplay between events via arguments and argument relations. We introduce the concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. Additionally, we propose a Temporal Event Graph Model that models the emergence of event instances following the temporal complex event schema. To build and evaluate such schemas, we release a new schema learning corpus containing 6,399 documents accompanied with event graphs, and manually constructed gold schemas. Intrinsic evaluation by schema matching and instance graph perplexity, prove the superior quality of our probabilistic graph schema library compared to linear representations. Extrinsic evaluation on schema-guided event prediction further demonstrates the predictive power of our event graph model, significantly surpassing human schemas and baselines by more than 17.8% on HITS@1.

To combat COVID-19, both clinicians and scientists need to digest the vast amount of relevant biomedical knowledge in literature to understand the disease mechanism and the related biological functions. We have developed a novel and comprehensive knowledge discovery framework, \textbf{COVID-KG} to extract fine-grained multimedia knowledge elements (entities, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures and knowledge subgraphs as evidence. All of the data, KGs, reports, resources and shared services are publicly available.

The accompanying video captures the multi-modal data displays and speech dialogue of a human Commander (C) and a human Robot Navigator (RN) tele-operating a mobile robot (R) in a remote, previously unexplored area. We describe unique challenges for automation of turn-taking and coordination processes observed in the data.

We seek to develop a robot that will be capable of teaming with humans to accomplish physical exploration tasks that would not otherwise be possible in dynamic, dangerous environments. For such tasks, a human commander needs to be able to communicate with a robot that moves out of sight and relays information back to the commander. What is the best way to determine how a human commander would interact in a multi-modal spoken dialog with such a robot to accomplish tasks? In this paper, we describe our initial approach to discovering a principled basis for coordinating turn-taking, perception, and navigational behavior of a robot in communication with a commander, by identifying decision phases in dialogs collected in a WoZ framework. We present two types of utterance annotation with examples applied to task-oriented dialog between a human commander and a human ``robot navigator'' who controls the physical robot in a realistic environment similar to expected actual conditions. We discuss core robot capabilities that bear on the robot navigator's ability to take turns while performing a ``find the building doors'' task at hand. The paper concludes with a brief overview of ongoing work to implement these decision phases within an open-source dialog management framework, constructing a task tree specification and dialog control logic for our application domain.