This workshop theme centers on the development of AI agents and systems that are capable of understanding, adapting to, and reacting to collaborate with humans in compliance with the social norms. These systems leverage insights from social psychology, cognitive science, robotics, and AI to interpret social cues, anticipate the needs of others, and coordinate actions effectively within dynamic and often unpredictable contexts. We focus on embedding social awareness into AI systems, leading to Cooperative Intelligence [23] which focuses on building trust and relationship between humans and intelligent systems, instead of focusing on functions to replace humans. This paradigm is expected to realize a hybrid society, where humans coexist with ubiquitous intelligent agents.

Vertical take-off and landing (VTOL) aircraft do not require a prolonged runway, thus allowing them to land almost anywhere. In recent years, their flexibility has made them popular in development, research, and operation. When compared to traditional fixed-wing aircraft and rotorcraft, VTOLs bring unique challenges as they combine many maneuvers from both types of aircraft. Pilot workload is a critical factor for safe and efficient operation of VTOLs. In this work, we conduct a user study to collect multimodal data from 28 pilots while they perform a variety of VTOL flight tasks. We analyze and interpolate behavioral patterns related to their performance and perceived workload. Finally, we build machine learning models to estimate their workload from the collected data. Our results are promising, suggesting that quantitative and accurate VTOL pilot workload monitoring is viable. Such assistive tools would help the research field understand VTOL operations and serve as a stepping stone for the industry to ensure VTOL safe operations and further remote operations.

A key factor to optimal acceptance and comfort of automated vehicle features is the driving style. Mismatches between the automated and the driver preferred driving styles can make users take over more frequently or even disable the automation features. This work proposes identification of user driving style preference with multimodal signals, so the vehicle could match user preference in a continuous and automatic way. We conducted a driving simulator study with 36 participants and collected extensive multimodal data including behavioral, physiological, and situational data. This includes eye gaze, steering grip force, driving maneuvers, brake and throttle pedal inputs as well as foot distance from pedals, pupil diameter, galvanic skin response, heart rate, and situational drive context. Then, we built machine learning models to identify preferred driving styles, and confirmed that all modalities are important for the identification of user preference. This work paves the road for implicit adaptive driving styles on automated vehicles.

The population around the globe is aging rapidly. People are living longer due to an increase in life expectancy and less young people become available to help the elderly population. Therefore, elderly people are facing functional and mental declines that affect their everyday activities and quality of life. Emergence of Socially Assistive Robots (SAR) in recent years and application of animal-like SARs in particular for elder care have shown positive effects including reduced stress, improved communication and social interaction among older adults. However, the existing animal-like SARs are generally passive and limited in terms of gesture-based interaction while the existing humanoid SARs have hard exteriors that prevent them from being in close proximity to the people to have touch-based interaction. In this paper, we have designed and developed a novel active SAR, Rassle, with whole body tactile sensing and movable limbs to take full advantage of touch-based interactions with older adults. Touching can create social bonding and it involves upper limb movement. We believe Rassle can encourage gross motor activity and deliver mental stimuli during interaction. In addition, we have conducted a system validation study with twelve unimpaired adults. Experimental results demonstrate Rassle's ability to deliver mental stimuli with a variety of difficulty levels and show that subjects enjoyed interacting with Rassle.