ICMI ’25, Canberra, ACT, Australia

In this work, we examine robots "asking for help" in unpredictable human spaces. We focus on an open question particularly relevant for robots deployed in public–"how do people help robots?" We present an elicitation study that shows how asking for help in a real-world field study yields valuable and sometimes unexpected information. From our study, we examine strangers’ responses toward a robot asking for spatial directions and extract valuable themes that can inform future asking-for-help systems. Our analysis provides a wide range of information, from geometric and topological information in natural language to details about rejection during an interaction. Further, we also provide anecdotes of valuable outlier behavior that can only be captured through a study in a real public space. Through our work, we highlight the importance of in-the-wild studies and discuss how the rich information they contribute will help robots effectively ask for help.

Interactive preference learning systems infer human preferences by presenting queries as pairs of options and collecting binary choices. Although binary choices are simple and widely used, they provide limited information about preference strength. To address this, we leverage human response times, which are inversely related to preference strength, as an additional signal. We propose a computationally efficient method that combines choices and response times to estimate human utility functions, grounded in the EZ diffusion model from psychology. Theoretical and empirical analyses show that for queries with strong preferences, response times complement choices by providing extra information about preference strength, leading to significantly improved utility estimation. We incorporate this estimator into preference-based linear bandits for fixed-budget best-arm identification. Simulations on three real-world datasets demonstrate that using response times significantly accelerates preference learning compared to choice-only approaches. Additional materials, such as code, slides, and talk video, are available at https://shenlirobot.github.io/pages/NeurIPS24.html.

Robots deployed in airports, malls, and stadiums today require expert oversight, pre-provided maps, and infrastructure. These systems are engineered to their specific deployment spaces and rely heavily on geometric maps for motion planning. In this work we consider a robot with only local-sensing and present a navigation strategy that uses signs and homotopy classes to fuel planning. We prove that in the worst-case, this strategy still maintains the probabilistic completeness that sampling based motion planners provide. Furthermore, we experimentally show that this exploration strategy results in 100% goal completion in real airport floor plans, and demonstrate how the robot’s sensing capabilities affects efficiency. We also show the effect of the environment variables, such as total number of obstacles, and density of obstacles, on navigation.

Robot navigation in human spaces today largely relies on the construction of precise geometric maps and a global motion plan. In this work, we navigate with only local sensing by using available signage - as designed for humans - in human-made environments such as airports. We propose a formalization of "signage" and define 4 levels of signage that we call complete, fully-specified, consistent and valid. The signage formalization can be used on many space skeletonizations, but we specifically provide an approach for navigation on the medial axis. We prove that we can achieve global completeness guarantees without requiring a global map to plan. We validate with two sets of experiments: (1) with real-world airports and their real signs and (2) real New York City neighborhoods. In (1) we show we can use real-world airport signage to improve on a simple random-walk approach, and we explore augmenting signage to further explore signs' impact on trajectory length. In (2), we navigate in varied sized subsets of New York City to show that, since we only use local sensing, our approach scales linearly with trajectory length rather than freespace area.

Humans expect their collaborators to look beyond the explicit interpretation of their words. Implicature is a common form of implicit communication that arises in natural language discourse when an utterance leverages context to imply information beyond what the words literally convey. Whereas computational methods have been proposed for interpreting and using different forms of implicature, its role in human and artificial agent collaboration has not yet been explored in a concrete domain. The results of this paper provide insights to how artificial agents should be structured to facilitate natural and efficient communication of actionable information with humans. We investigated implicature by implementing two strategies for playing Hanabi, a cooperative card game that relies heavily on communication of actionable implicit information to achieve a shared goal. In a user study with 904 completed games and 246 completed surveys, human players randomly paired with an implicature AI are 71% more likely to think their partner is human than players paired with a non-implicature AI. These teams demonstrated game performance similar to other state of the art approaches.

Robots must be cognizant of how their actions will be interpreted in context. Actions performed in the context of a joint activity comprise two aspects: functional and communicative. The functional component achieves the goal of the action, whereas its communicative component, when present, expresses some information to the actor's partners in the joint activity. The interpretation of such communication requires leveraging information that is public to all participants, known as common ground. Much of human communication is performed through this implicit mechanism, and humans cannot help but infer some meaning -- whether or not it was intended by the actor -- from most actions. We present a framework for robots to utilize this communicative channel on top of normal functional actions to work more effectively with human partners. We consider the role of the actor and the observer, both individually and jointly, in implicit communication, as well as the effects of timing. We also show how the framework maps onto various modes of action, including natural language and motion. We consider these modes of action in various human-robot interaction domains, including social navigation and collaborative assembly.