Scholarly Works, Center for Human-Computer Interaction (CHCI)
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Browsing Scholarly Works, Center for Human-Computer Interaction (CHCI) by Content Type "Article"
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- Here’s What I’ve Learned: Asking Questions that Reveal Reward LearningHabibian, Soheil; Jonnavittula, Ananth; Losey, Dylan P. (Virginia Tech, 2021-07-02)Robots can learn from humans by asking questions. In these questions the robot demonstrates a few different behaviors and asks the human for their favorite. But how should robots choose which questions to ask? Today’s robots optimize for informative questions that actively probe the human’s preferences as efficiently as possible. But while informative questions make sense from the robot’s perspective, human onlookers often find them arbitrary and misleading. For example, consider an assistive robot learning to put away the dishes. Based on your answers to previous questions this robot knows where it should stack each dish; however, the robot is unsure about right height to carry these dishes. A robot optimizing only for informative questions focuses purely on this height: it shows trajectories that carry the plates near or far from the table, regardless of whether or not they stack the dishes correctly. As a result, when we see this question, we mistakenly think that the robot is still confused about where to stack the dishes! In this paper we formalize active preference-based learning from the human’s perspective. We hypothesize that — from the human’s point-of-view — the robot’s questions reveal what the robot has and has not learned. Our insight enables robots to use questions to make their learning process transparent to the human operator.We develop and test a model that robots can leverage to relate the questions they ask to the information these questions reveal. We then introduce a trade-off between informative and revealing questions that considers both human and robot perspectives: a robot that optimizes for this trade-off actively gathers information from the human while simultaneously keeping the human up to date with what it has learned. We evaluate our approach across simulations, online surveys, and in-person user studies. We find that robots which consider the human’s point of view learn just as quickly as state-of-the-art baselines while also communicating what they have learned to the human operator. Videos of our user studies and results are available here: https://youtu.be/tC6y_jHN7Vw.
- I Know What You Meant: Learning Human Objectives by (Under)estimating Their Choice SetJonnavittula, Ananth; Losey, Dylan P. (Virginia Tech, 2021-04-05)Assistive robots have the potential to help people perform everyday tasks. However, these robots first need to learn what it is their user wants them to do. Teaching assistive robots is hard for inexperienced users, elderly users, and users living with physical disabilities, since often these individuals are unable to show the robot their desired behavior. We know that inclusive learners should give human teachers credit for what they cannot demonstrate. But today’s robots do the opposite: they assume every user is capable of providing any demonstration. As a result, these robots learn to mimic the demonstrated behavior, even when that behavior is not what the human really meant! Here we propose a different approach to reward learning: robots that reason about the user’s demonstrations in the context of similar or simpler alternatives. Unlike prior works — which err towards overestimating the human’s capabilities — here we err towards underestimating what the human can input (i.e., their choice set). Our theoretical analysis proves that underestimating the human’s choice set is risk-averse, with better worst-case performance than overestimating. We formalize three properties to generate similar and simpler alternatives. Across simulations and a user study, our resulting algorithm better extrapolates the human’s objective. See the user study here: https://youtu.be/RgbH2YULVRo.
- Learning to Share Autonomy Across Repeated InteractionJonnavittula, Ananth; Losey, Dylan P. (Virginia Tech, 2021-07-20)Wheelchair-mounted robotic arms (and other assistive robots) should help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot maintain control over the robot’s motion: as the robot becomes confident it understands what the human wants, it increasingly intervenes to automate the task. But how does the robot know what tasks the human may want to perform in the first place? Today’s shared autonomy approaches often rely on prior knowledge: for example, the robot must know the set of possible human goals a priori. In the long-term, however, this prior knowledge will inevitably break down — sooner or later the human will reach for a goal that the robot did not expect. In this paper we propose a learning approach to shared autonomy that takes advantage of repeated interactions. Learning to assist humans would be impossible if they performed completely different tasks at every interaction: but our insight is that users living with physical disabilities repeat important tasks on a daily basis (e.g., opening the fridge, making coffee, and having dinner). We introduce an algorithm that exploits these repeated interactions to recognize the human’s task, replicate similar demonstrations, and return control when unsure. As the human repeatedly works with this robot, our approach continually learns to assist tasks that were never specified beforehand: these tasks include both discrete goals (e.g., reaching a cup) and continuous skills (e.g., opening a drawer). Across simulations and an in-person user study, we demonstrate that robots leveraging our approach match existing shared autonomy methods for known goals, and outperform imitation learning baselines on new tasks. See videos here: https://youtu.be/NazeLVbQ2og.
- Virtual replicas of real places: Experimental investigationsSkarbez, Richard; Bowman, Douglas A.; Ogle, J. Todd; Tucker, Thomas; Gabbard, Joseph L. (2021-07-13)The emergence of social virtual reality (VR) experiences, such as Facebook Spaces, Oculus Rooms, and Oculus Venues, will generate increased interest from users who want to share real places (both personal and public) with their fellow users in VR. At the same time, advances in scanning and reconstruction technology are making the realistic capture of real places more and more feasible. These complementary pressures mean that the representation of real places in virtual reality will be an increasingly common use case for VR. Despite this, there has been very little research into how users perceive such replicated spaces. This paper reports the results from a series of three user studies investigating this topic. Taken together, these results show that getting the scale of the space correct is the most important factor for generating a "feeling of reality", that it is important to avoid incoherent behaviors (such as floating objects), and that lighting makes little difference to perceptual similarity.