Gaze behavior is widely treated as a trainable component of high-performance driving, yet its causal role in performance remains unclear. We tested whether enforcing expert gaze patterns improves circuit driving performance in a simulator study with 60 participants assigned to free gaze, skilled-gaze guidance, or novice-gaze guidance. Gaze guidance replayed naturalistic gaze trajectories from actual skilled and novice drivers during training, followed by an unguided retention session. Despite clear compliance with gaze instructions during training, gaze guidance had no significant effect on lap time, steering or pedal smoothness, or lateral deviation from an optimal racing line, aside from a minimal per-corner difference. Performance improvements were attributable to practice and persisted independently of gaze condition. These findings suggest that gaze guidance alone is insufficient to improve high-performance driving, underscoring the need for training approaches that integrate it with additional instruction or feedback.

How can I improve my performance here?Figure 1: After completing a lap, the driver pauses the video at a specific segment and asks ApexTrainer for feedback.The system generates context-aware suggestions based on the referenced location.

Learning motor skills for sports or performance driving is often done with professional instruction from expert human teachers, whose availability is limited. Our goal is to enable automated teaching via a learned model that interacts with the student similar to a human teacher. However, training such automated teaching systems is limited by the availability of highquality annotated datasets of expert teacher and student interactions as they are difficult to collect at scale. To address this data scarcity problem, we propose an approach for training a coaching system for complex motor tasks such as high performance driving via a Multi-Task Imitation Learning (MTIL) paradigm. MTIL allows our model to learn robust representations by utilizing self-supervised training signals from more readily available non-interactive datasets of humans performing the task of interest. We validate our approach with (1) a semi-synthetic dataset created from real human driving trajectories, (2) a professional track driving instruction dataset, (3) a track-racing driving simulator human-subject study, and (4) a system demonstration on an instrumented car at a race track. Our experiments show that the right set of auxiliary machine learning tasks improves prediction of teaching instructions. Moreover, in the human subjects study, students exposed to the instructions from our teaching system improve their ability to stay within track limits, and show favorable perception of the model's interaction with them, in terms of usefulness and satisfaction.

Drifting, a skillful driving technique involving intentional traction loss and counter-steering, traditionally demands high-speed maneuvers under high-friction conditions, posing significant risks and fear for novices. Our study explores low-µ (low friction) emulation, simulating icy conditions to facilitate drift training at safer, lower speeds. This approach not only enhances safety and mitigates fear by reducing the required speed for drifting, but also extends the time for them to react. A between-group design was employed, comparing drift training outcomes between participants trained exclusively in higher-µ conditions (control group) and those who trained initially in lower-µ conditions before transitioning to higher-µ conditions (target group). The performance was assessed through the average distance of continuous sliding, along with subjective measures of motivation and workload. The results showed that the target group achieved greater slide distances in the retention session and reported higher scores on the positive intrinsic motivation factors, suggesting enhanced performance and engagement.

Should fully autonomous vehicles (FAVs) be designed inclusively and accessibly, independence will be transformed for millions of people experiencing transportation-limiting disabilities worldwide. Although FAVs hold promise to improve efficient transportation without intervention, a truly accessible experience must enable user input, for all people, in many driving scenarios (e.g., to alter a route or pull over during an emergency). Therefore, this paper explores desires for control in FAVs among (n=23) people who are blind and visually impaired. Results indicate strong support for control across a battery of driving tasks, as well as the need for multimodal information. These findings inspired the design and evaluation of a novel multisensory interface leveraging mid-air gestures, audio, and haptics. All participants successfully navigated driving scenarios using our gestural-audio interface, reporting high ease-of-use. Contributions include the first inclusively designed gesture set for FAV control and insight regarding supplemental haptic and audio cues.

Desired model behavior often differs across contexts (e.g., different geographies, communities, or institutions), but there is little infrastructure to facilitate context-specific evaluations key to deployment decisions and building trust. Here, we present Kaleidoscope, a system for evaluating models in terms of user-driven, domain-relevant concepts. Kaleidoscope’s iterative workflow enables generalizing from a few examples into a larger, diverse set representing an important concept. These example sets can be used to test model outputs or shifts in model behavior in semantically-meaningful ways. For instance, we might construct a “xenophobic comments” set and test that its examples are more likely to be flagged by a content moderation model than a “civil discussion” set. To evaluate Kaleidoscope, we compare it against template- and DSL-based grouping methods, and conduct a usability study with 13 Reddit users testing a content moderation model. We find that Kaleidoscope facilitates iterative, exploratory hypothesis testing across diverse, conceptually-meaningful example sets.

Automated vehicle control beyond friction limits is an emerging technology that can further improve vehicle safety. This paper focuses on the driver's understanding of steering behavior during automated counter-steering. We hypothesize that: 1) motorsports fans or racing game players (GP) have a correct mental model of automated counter-steering, while 2) ordinary drivers (Non-GP) do not, and 3) Non-GP incorrectly estimate rotation direction as being always consistent between the steering wheel and vehicle. We first propose a method to examine the accuracy of a driver's steering mental model. The method measures the participants' ability to estimate steering wheel rotation from a video only showing vehicle rotation and vice versa. We compared the performance of GP and Non-GP using videos with and without counter-steering. The results only support hypothesis 2. This indicates that automated counter-steering likely confuses most drivers no matter their prior knowledge, which makes the improvement of driver-vehicle communication critical.

Many older adults wish to remain in their own homes as they age [16]. However, challenges in performing home upkeep tasks threaten an older adult's ability to age in place. Even healthy independently living older adults experience challenges in maintaining their home [13]. Challenges with home tasks can be compensated through technology, such as home robots. However, for home robots to be adopted by older adult users, they must be designed to meet older adults' needs for assistance and the older users must be amenable to robot assistance for those needs. We conducted a needs assessment to (1) assess older adults' openness to assistance from robots; and (2) understand older adults' opinions about using an assistive robot to help around the home. We administered questionnaires and conducted structured group interviews with 21 independently living older adults (ages 65-93). The questionnaire data suggest that older adults prefer robot assistance for cleaning and fetching/organizing tasks overall. However their assistance preferences discriminated between tasks. The interview data provided insight as to why they hold such preferences. Older adults reported benefits of robot assistance (e.g., the robot compensating for limitations, saving them time and effort, completing undesirable tasks, and performing tasks at a high level of performance). Participants also reported concerns such as the robot damaging the environment, being unreliable at or incapable of doing a task, doing tasks the older adult would rather do, or taking up too much space/storage. These data, along with specific comments from participant interviews, provide the basis for preliminary recommendations for designing mobile manipulator robots to support aging in place.

The Robots for Humanity project aims to enable people with severe motor impairments to interact with their own bodies and their environment through the use of an assistive mobile manipulator, thereby improving their quality of life. Assistive mobile manipulators (AMMs) are mobile robots that physically manipulate the world in order to provide assistance to people with disabilities. They present an exciting frontier for assistive technology, as they can operate away from the user, have a large dexterous workspace (due to their mobility), and not directly encumber their users. The cornerstone of this project is an ongoing, interactive design process with a quadriplegic user, Henry Evans, and his wife and primary caregiver, Jane Evans. Henry has been enabled, through the use of a PR2 robot, to scratch his own face, shave, fetch a towel from his kitchen, and hand out Halloween candy to trick-ortreating children at a local mall.

Hygiene and feeding are activities of daily living (ADLs) that often involve contact with a person's face. Robots can assist people with motor impairments to perform these tasks by holding a tool that makes contact with the care receiver's face. By sensing the forces applied to the face with the tool, robots could potentially provide assistance that is more comfortable, safe, and effective. In order to inform the design of robotic controllers and assistive robots, we investigated the forces able-bodied people apply to themselves when wiping and shaving their faces. We present our methods for capturing and modeling these forces, results from a study with 9 participants, and recommendations for assistive robots. Our contributions include a trapezoidal force model that assumes participants have a target force they attempt to achieve for each stroke of the tool. We discuss advantages of this 3 parameter model and show that it fits our data well relative to other candidate models. We also provide statistics of the models' rise rates, fall rates, and target forces for the 9 participants in our study. In addition, we illustrate how the target forces varied based on the task, participant, and location on the face.

By initiating physical contact with people, robots can be more useful. For example, a robotic caregiver might make contact to provide physical assistance or facilitate communication. So as to better understand how people respond to robot-initiated touch, we conducted a 2x2 between-subjects experiment with 56 people in which a robotic nurse autonomously touched and wiped the subject's forearm. Our independent variables were whether or not the robot verbally warned the person before contact, and whether the robot verbally indicated that the touch was intended to clean the person's skin (instrumental touch) or to provide comfort (affective touch). On average, regardless of the treatment, participants had a generally positive subjective response. However, with instrumental touch people responded significantly more favorably. Since the physical behavior of the robot was the same for all trials, our results demonstrate that the perceived intent of the robot can significantly influence a person's subjective response to robot-initiated touch. Our results suggest that roboticists should consider this factor in addition to the mechanics of physical interaction. Unexpectedly, we found that participants tended to respond more favorably without a verbal warning. Although inconclusive, our results suggest that verbal warnings prior to contact should be carefully designed, if used at all.

When a user is in close proximity to a robot, physical contact becomes a potentially valuable channel for communication. People often use direct physical contact to guide a person to a desired location (e.g., leading a child by the hand) or to adjust a person's posture for a task (e.g., a dance instructor working with a dancer). Within this paper, we present an implementation and evaluation of a direct physical interface for a human-scale anthropomorphic robot. We define a direct physical interface (DPI) to be an interface that enables a user to influence a robot's behavior by making contact with its body. Human-human interaction inspired our interface design, which enables a user to lead our robot by the hand and position its arms. We evaluated this interface in the context of assisting nurses with patient lifting, which we expect to be a high-impact application area. Our evaluation consisted of a controlled laboratory experiment with 18 nurses from the Atlanta area of Georgia, USA. We found that our DPI significantly outperformed a comparable wireless gamepad interface in both objective and subjective measures, including number of collisions, time to complete the tasks, workload (Raw Task Load Index), and overall preference. In contrast, we found no significant difference between the two interfaces with respect to the users' perceptions of personal safety.

This paper describes the design and implementation of a behavior that allows a robot with a compliant arm to perform wiping motions that are involved in bed baths. A laser-based operator-selection interface enables an operator to select an area to clean, and the robot autonomously performs a wiping motion using equilibrium point control. We evaluated the performance of the system by measuring the ability of the robot to remove an area of debris on human skin. We tested the performance of the behavior algorithm by commanding the robot to wipe off a 1-inch square area of debris placed on the surface of the upper arm, forearm, thigh, and shank of a human subject. Using image processing, we determined the hue content of the debris and used this representation to determine the percentage of debris that remained on the arm after the robot completed the task. In our experiments, the robot removed most of the debris (>96%) on four parts of the limbs. In addition, the robot performed the wiping task using relatively low force (<;3 N).

Delivering an object to a user would be a generally useful capability for service robots. Within this paper, we look at this capability in the context of assistive object retrieval for motor-impaired users. We first describe a behavior-based system that enables our mobile robot EL-E to autonomously deliver an object to a motor-impaired user. We then present our evaluation of this system with 8 motor-impaired patients from the Emory ALS Center. As part of this study, we compared handing the object to the user (direct delivery) with placing the object on a nearby table (indirect delivery). We tested the robot delivering a cordless phone, a medicine bottle, and a TV remote, which were ranked as three of the top four most important objects for robotic delivery by ALS patients in a previous study. Overall, the robot successfully delivered these objects in 126 out of 144 trials (88%) with a success rate of 97% for indirect delivery and 78% for direct delivery. In an accompanying survey, participants showed high satisfaction with the robot with 4 people preferring direct delivery and 4 people preferring indirect delivery. Our results indicate that indirect delivery to a surface can be a robust and reliable delivery method with high user satisfaction, and that robust direct delivery will require methods that handle diverse postures and body types.