Tom Williams

Zhao Han, Tom Williams

Within the human-robot interaction (HRI) community, many researchers have focused on the careful design of human-subjects studies. However, other parts of the community, e.g., the technical advances community, also need to do human-subjects studies to collect data to train their models, in ways that require user studies but without a strict experimental design. The design of such data collection is an underexplored area worthy of more attention. In this work, we contribute a clearly defined process to collect data with three steps for machine learning modeling purposes, grounded in recent literature, and detail an use of this process to facilitate the collection of a corpus of referring expressions. Specifically, we discuss our data collection goal and how we worked to encourage well-covered and abundant participant responses, through our design of the task environment, the task itself, and the study procedure. We hope this work would lead to more data collection formalism efforts in the HRI community and a fruitful discussion during the workshop.

Zhao Han, Jenna Parrillo, Alexander Wilkinson et al.

For mobile robots, mobile manipulators, and autonomous vehicles to safely navigate around populous places such as streets and warehouses, human observers must be able to understand their navigation intent. One way to enable such understanding is by visualizing this intent through projections onto the surrounding environment. But despite the demonstrated effectiveness of such projections, no open codebase with an integrated hardware setup exists. In this work, we detail the empirical evidence for the effectiveness of such directional projections, and share a robot-agnostic implementation of such projections, coded in C++ using the widely-used Robot Operating System (ROS) and rviz. Additionally, we demonstrate a hardware configuration for deploying this software, using a Fetch robot, and briefly summarize a full-scale user study that motivates this configuration. The code, configuration files (roslaunch and rviz files), and documentation are freely available on GitHub at https://github.com/umhan35/arrow_projection.

Casey Kennington, Malihe Alikhani, Heather Pon-Barry et al. · cmu

The ability to interact with machines using natural human language is becoming not just commonplace, but expected. The next step is not just text interfaces, but speech interfaces and not just with computers, but with all machines including robots. In this paper, we chronicle the recent history of this growing field of spoken dialogue with robots and offer the community three proposals, the first focused on education, the second on benchmarks, and the third on the modeling of language when it comes to spoken interaction with robots. The three proposals should act as white papers for any researcher to take and build upon.

Michael Walker, Thao Phung, Tathagata Chakraborti et al.

Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in research in recent years. However, the HRI community lacks a set of shared terminology and framework for characterizing aspects of mixed reality interfaces, presenting serious problems for future research. Therefore, it is important to have a common set of terms and concepts that can be used to precisely describe and organize the diverse array of work being done within the field. In this paper, we present a novel taxonomic framework for different types of VAM-HRI interfaces, composed of four main categories of virtual design elements (VDEs). We present and justify our taxonomy and explain how its elements have been developed over the last 30 years as well as the current directions VAM-HRI is headed in the coming decade.

Tom Williams, Ruchen Wen

Social Justice oriented Engineering Education frameworks have been developed to help guide engineering students' decisions about which projects will genuinely address human needs to create a better and more equitable society. In this paper, we explore the role such theories might play in the field of AI-HRI, consider the extent to which our community is (or is not) aligned with these recommendations, and envision a future in which our research community takes guidance from these theories. In particular, we analyze recent AI-HRI (through analysis of 2020 AI-HRI papers) and consider possible futures of AI-HRI (through a speculative ethics exercise). Both activities are guided through the lens of the Engineering for Social Justice (E4SJ) framework, which centers contextual listening and enhancement of human capabilities. Our analysis suggests that current AI-HRI research is not well aligned with the guiding principles of Engineering for Social Justice, and as such, does not obviously meet the needs of the communities we could be helping most. As such, we suggest that motivating future work through the E4SJ framework could help to ensure that we as researchers are developing technologies that will actually lead to a more equitable world.

Nichole D. Starr, Bertram Malle, Tom Williams

Due to their unique persuasive power, language-capable robots must be able to both act in line with human moral norms and clearly and appropriately communicate those norms. These requirements are complicated by the possibility that humans may ascribe blame differently to humans and robots. In this work, we explore how robots should communicate in moral advising scenarios, in which the norms they are expected to follow (in a moral dilemma scenario) may be different from those their advisees are expected to follow. Our results suggest that, in fact, both humans and robots are judged more positively when they provide the advice that favors the common good over an individual's life. These results raise critical new questions regarding people's moral responses to robots and the design of autonomous moral agents.

Poulomi Pal, Tom Williams

Language-capable interactive robots participating in dialogues with human interlocutors must be able to naturally and efficiently communicate about the entities in their environment. A key aspect of such communication is the use of anaphoric language. The linguistic theory of the Givenness Hierarchy(GH) suggests that humans use anaphora based on the cognitive statuses their referents have in the minds of their interlocutors. In previous work, researchers presented GH-theoretic approaches to robot anaphora understanding. In this paper we describe how the GH might need to be used quite differently to facilitate robot anaphora generation.

Tom Williams, Torin Johnson, Will Culpepper et al.

To engage in human-like dialogue, robots require the ability to describe the objects, locations, and people in their environment, a capability known as "Referring Expression Generation." As speakers repeatedly refer to similar objects, they tend to re-use properties from previous descriptions, in part to help the listener, and in part due to cognitive availability of those properties in working memory (WM). Because different theories of working memory "forgetting" necessarily lead to differences in cognitive availability, we hypothesize that they will similarly result in generation of different referring expressions. To design effective intelligent agents, it is thus necessary to determine how different models of forgetting may be differentially effective at producing natural human-like referring expressions. In this work, we computationalize two candidate models of working memory forgetting within a robot cognitive architecture, and demonstrate how they lead to cognitive availability-based differences in generated referring expressions.

Poulomi Pal, Lixiao Zhu, Andrea Golden-Lasher et al.

For language-capable interactive robots to be effectively introduced into human society, they must be able to naturally and efficiently communicate about the objects, locations, and people found in human environments. An important aspect of natural language communication is the use of pronouns. Ac-cording to the linguistic theory of the Givenness Hierarchy(GH), humans use pronouns due to implicit assumptions about the cognitive statuses their referents have in the minds of their conversational partners. In previous work, Williams et al. presented the first computational implementation of the full GH for the purpose of robot language understanding, leveraging a set of rules informed by the GH literature. However, that approach was designed specifically for language understanding,oriented around GH-inspired memory structures used to assess what entities are candidate referents given a particular cognitive status. In contrast, language generation requires a model in which cognitive status can be assessed for a given entity. We present and compare two such models of cognitive status: a rule-based Finite State Machine model directly informed by the GH literature and a Cognitive Status Filter designed to more flexibly handle uncertainty. The models are demonstrated and evaluated using a silver-standard English subset of the OFAI Multimodal Task Description Corpus.

Justin W. Hart, Nick DePalma, Richard G. Freedman et al.

The past few years have seen rapid progress in the development of service robots. Universities and companies alike have launched major research efforts toward the deployment of ambitious systems designed to aid human operators performing a variety of tasks. These robots are intended to make those who may otherwise need to live in assisted care facilities more independent, to help workers perform their jobs, or simply to make life more convenient. Service robots provide a powerful platform on which to study Artificial Intelligence (AI) and Human-Robot Interaction (HRI) in the real world. Research sitting at the intersection of AI and HRI is crucial to the success of service robots if they are to fulfill their mission. This symposium seeks to highlight research enabling robots to effectively interact with people autonomously while modeling, planning, and reasoning about the environment that the robot operates in and the tasks that it must perform. AI-HRI deals with the challenge of interacting with humans in environments that are relatively unstructured or which are structured around people rather than machines, as well as the possibility that the robot may need to interact naturally with people rather than through teach pendants, programming, or similar interfaces.

Tom Williams, Ravenna Thielstrom, Evan Krause et al.

Human-robot communication in situated environments involves a complex interplay between knowledge representations across a wide variety of modalities. Crucially, linguistic information must be associated with representations of objects, locations, people, and goals, which may be represented in very different ways. In previous work, we developed a Consultant Framework that facilitates modality-agnostic access to information distributed across a set of heterogeneously represented knowledge sources. In this work, we draw inspiration from cognitive science to augment these distributed knowledge sources with Short Term Memory Buffers to create an STM-augmented algorithm for referring expression generation. We then discuss the potential performance benefits of this approach and insights from cognitive science that may inform future refinements in the design of our approach.

Daniel Kasenberg, Vasanth Sarathy, Thomas Arnold et al.

In this paper we describe moral quasi-dilemmas (MQDs): situations similar to moral dilemmas, but in which an agent is unsure whether exploring the plan space or the world may reveal a course of action that satisfies all moral requirements. We argue that artificial moral agents (AMAs) should be built to handle MQDs (in particular, by exploring the plan space rather than immediately accepting the inevitability of the moral dilemma), and that MQDs may be useful for evaluating AMA architectures.

Eric Eaton, Sven Koenig, Claudia Schulz et al.

The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia). As part of the program, awardees were asked to address one of the following "blue sky" questions: * How could/should Artificial Intelligence (AI) courses incorporate ethics into the curriculum? * How could we teach AI topics at an early undergraduate or a secondary school level? * AI has the potential for broad impact to numerous disciplines. How could we make AI education more interdisciplinary, specifically to benefit non-engineering fields? This paper is a collection of their responses, intended to help motivate discussion around these issues in AI education.