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Intelligent driving assistance can alert drivers to objects in their environment; however, such systems require a model of drivers' situational awareness (SA) (what aspects of the scene they are already aware of) to avoid unnecessary alerts. Moreover, collecting the data to train such an SA model is challenging: being an internal human cognitive state, driver SA is difficult to measure, and non-verbal signals such as eye gaze are some of the only outward manifestations of it. Traditional methods to obtain SA labels rely on probes that result in sparse, intermittent SA labels unsuitable for modeling a dense, temporally correlated process via machine learning. We propose a novel interactive labeling protocol that captures dense, continuous SA labels and use it to collect an object-level SA dataset in a VR driving simulator. Our dataset comprises 20 unique drivers' SA labels, driving data, and gaze (over 320 minutes of driving) which will be made public. Additionally, we train an SA model from this data, formulating the object-level driver SA prediction problem as a semantic segmentation problem. Our formulation allows all objects in a scene at a timestep to be processed simultaneously, leveraging global scene context and local gaze-object relationships together. Our experiments show that this formulation leads to improved performance over common sense baselines and prior art on the SA prediction task. 

Human-robot interaction is now an established discipline. Dozens of HRI courses exist at universities worldwide, and some institutions even offer degrees in HRI. However, although many students are being taught HRI, there is no agreed-upon curriculum for an introductory HRI course. In this workshop, we aim to reach community consensus on what should be covered in such a course. Through interactive activities like panels, breakout discussions, and syllabus design, workshop participants will explore the many topics and pedagogical approaches for teaching HRI. They will then distill their findings into a single example introductory HRI curriculum. Output from this workshop will include a short paper explaining this curriculum and an example syllabus that can be used and adapted by HRI educators. 

Augmentative and alternative communication (AAC) devices enable speech-based communication, but generating speech is not the only resource needed to have a successful conversation. Being able to signal one wishes to take a turn by raising a hand or providing some other cue is critical in securing a turn to speak. Experienced conversation partners know how to recognize the nonverbal communication an augmented communicator (AC) displays, but these same nonverbal gestures can be hard to interpret by people who meet an AC for the first time. Prior work has identified motion through robots and expressive objects as a modality that can support communication. In this work, we work closely with an AAC user to understand how motion through a physical expressive object can support their communication. We present our process and resulting lessons on the designed object and the co-design process. 

We designed an observer-aware method for creating navigation paths that simultaneously indicate a robot’s goal while attempting to remain in view for a particular observer. Prior art in legible motion does not account for the limited field of view of observers, which can lead to wasted communication efforts that are unobserved by the intended audience. Our observer-aware legibility algorithm directly models the locations and perspectives of observers, and places legible movements where they can be easily seen. To explore the effectiveness of this technique, we performed a 300-person online user study. Users viewed first-person videos of restaurant scenes with robot waiters moving along paths optimized for different observer perspectives, along with a baseline path that did not take into account any observer’s field of view. Participants were asked to report their estimate of how likely it was the robot was heading to their table versus the other goal table as it moved along each path. We found that for observers with incomplete views of the restaurant, observer-aware legibility is effective at increasing the period of time for which observers correctly infer the goal of the robot. Non-targeted observers have lower performance on paths created for other observers than themselves, which is the natural drawback of personalizing legible motion to a particular observer. We also find that an observer’s relationship to the environment (e.g. what is in their field of view) has more influence on their inferences than the observer’s relative position to the targeted observer, and discuss how this implies knowledge of the environment is required in order to effectively plan for multiple observers at once. 

As assistive robotics has expanded to many task domains, comparing assistive strategies among the varieties of research becomes increasingly difficult. To begin to unify the disparate domains into a more general theory of assistance, we present a definition of assistance, a survey of existing work, and three key design axes that occur in many domains and benefit from the examination of assistance as a whole. We first define an assistance perspective that focuses on understanding a robot that is in control of its actions but subordinate to a user’s goals. Next, we use this perspective to explore design axes that arise from the problem of assistance more generally and explore how these axes have comparable trade-offs across many domains. We investigate how the assistive robot handles other people in the interaction, how the robot design can operate in a variety of action spaces to enact similar goals, and how assistive robots can vary the timing of their actions relative to the user’s behavior. While these axes are by no means comprehensive, we propose them as useful tools for unifying assistance research across domains and as examples of how taking a broader perspective on assistance enables more cross-domain theorizing about assistance. 

Shared control systems can make complex robot teleoperation tasks easier for users. These systems predict the user’s goal, determine the motion required for the robot to reach that goal, and combine that motion with the user’s input. Goal prediction is generally based on the user’s control input (e.g., the joystick signal). In this paper, we show that this prediction method is especially effective when users follow standard noisily optimal behavior models. In tasks with input constraints like modal control, however, this effectiveness no longer holds, so additional sources for goal prediction can improve assistance. We implement a novel shared control system that combines natural eye gaze with joystick input to predict people’s goals online, and we evaluate our system in a real-world, COVID-safe user study. We find that modal control reduces the efficiency of assistance according to our model, and when gaze provides a prediction earlier in the task, the system’s performance improves. However, gaze on its own is unreliable and assistance using only gaze performs poorly. We conclude that control input and natural gaze serve different and complementary roles in goal prediction, and using them together leads to improved assistance. 

We present the Human And Robot Multimodal Observations of Natural Interactive Collaboration (HARMONIC) dataset. This is a large multimodal dataset of human interactions with a robotic arm in a shared autonomy setting designed to imitate assistive eating. The dataset provides human, robot, and environmental data views of 24 different people engaged in an assistive eating task with a 6-degree-of-freedom (6-DOF) robot arm. From each participant, we recorded video of both eyes, egocentric video from a head-mounted camera, joystick commands, electromyography from the forearm used to operate the joystick, third-person stereo video, and the joint positions of the 6-DOF robot arm. Also included are several features that come as a direct result of these recordings, such as eye gaze projected onto the egocentric video, body pose, hand pose, and facial keypoints. These data streams were collected specifically because they have been shown to be closely related to human mental states and intention. This dataset could be of interest to researchers studying intention prediction, human mental state modeling, and shared autonomy. Data streams are provided in a variety of formats such as video and human-readable CSV and YAML files.