Events

NSF will be hosting an NSF AI Day on September 18 and an AI Hill Day on September 19, 2023. The two events will highlight each of the NSF AI Institute accomplishments and allow room to interact with Members of Congress.

To see agenda, event details and sign up information, please visit: https://community.cmu.edu/customquickevents?id=a3p2S000000NRaB&custom=true

All participants must register for this event. Additional information will be provided in a confirmation email.

Register by Monday, July 24

Questions? contact@ai-caring.org

The schedule for Tuesday, the 15th, is primarily determined by the NSF and will consist of a combination of talks, meetings, and a poster session. On Wednesday, the 16th, we will be hosting an Industry Day where we will engage with a select group of industry partners. The day will culminate in a social hour in the evening. Please note that there will be simultaneous events for both the NSF Site Visit Team (SRT) conducting our review and the other attendees.

Find schedule and agenda here: https://drive.google.com/drive/folders/1WEoMsMlOjuOHZb7O4RLFlVLGFaHGP9ww?usp=sharing

Be sure to RSVP at https://docs.google.com/forms/d/e/1FAIpQLSfcg6OMbzRPh18w8Ur45_6PGNTF1I8RapUmFsocMPrG7yTkeQ/viewform

Upol Ehsan (GT), Philipp Wintersberger (TU Wien Austria), Elizabeth Anne Watkins (Intel Labs), Carina Manger (Technische Hochschule Ingolstadt), Gonzalo Ramos (Microsoft Research), Justin D. Weisz (IBM Research), Hal Daumé III (University of Maryland & Microsoft Research), Andreas Riener (Technische Hochschule Ingolstadt), Mark Riedl (GT).

Explainability is an essential pillar of Responsible AI. Explanations can improve real-world efficacy, provide harm mitigation levers, and can serve as a primary means to ensure humans’ right to understand and contest decisions made about them by AI systems. In ensuring this right, XAI can foster equitable, efficient, and resilient Human-AI collaboration. In this workshop, we serve as a junction point of cross-disciplinary stakeholders of the XAI landscape, from designers to engineers, from researchers to end-users. The goal is to examine how human-centered perspectives in XAI can be operationalized at the conceptual, methodological, and technical levels. Consequently, we call for position papers making justifiable arguments (up to 4 pages excluding references) that address topics involving the who (e.g., relevant diverse stakeholders), why (e.g., social/individual factors influencing explainability goals), when (e.g., when to trust the AI’s explanations vs. not) or where (e.g., diverse application areas, XAI for actionability or human-AI collaboration, or XAI evaluation). Papers should follow the CHI Extended Abstract format and be submitted through the workshop’s submission site (https://hcxai.jimdosite.com/).

The upcoming second annual AI-CARING Student Symposium is scheduled to take place at CMU on March 30-31, 2023. Organized by the student leadership council, the symposium promises a diverse array of activities including presentations, poster sessions, team-building exercises, and breakout sessions aimed at fostering collaboration and enhancing institutional frameworks. Notably, Dr. Juleen Rodakowski, an esteemed Associate Professor and Chair of the Department of Occupational Therapy at the University of Pittsburgh, will be leading an engaging session titled "Diagnosis and Assessment in individuals living with Mild Cognitive Impairment," which is expected to generate extensive discussion among AI-CARING students. Additionally, numerous breakout sessions will concentrate on identifying avenues for project integration, data sharing, and the development of shared deployments.

The 2022 Stretch Robot Pitch Competition, presented in partnership between the Rehabilitation Engineering Research Center on Technologies to Support Aging-in-Place for People with Long-Term Disabilities (RERC TechSAge), Hello Robot, and AI-CARING, took place at Georgia Tech. RERC TechSAge is a collaborative grant center encompassing Georgia Institute of Technology, University of Illinois Urbana-Champaign, and Georgia State University. It focuses on multidisciplinary research, development, and training projects aimed at understanding the needs of individuals aging with long-term vision, hearing, and mobility disabilities while developing supportive technologies.

Co-organized by Maithili Patel (GT), Sonia Chernova (GT), Fethiye Irmak Dogan (KTH, AI-CARING visiting scholar), Zhen Zeng (J.P.Morgan AI Research), Kim Baraka(Vrije Universiteit Amsterdam).
Service robots will be co-located with human users in an unstructured human-centered environment and will benefit from understanding the user's daily activities, preferences, and needs towards fully assisting them. In doing so, objects in the user’s environment can provide useful context in understanding and grounding information regarding the user's instructions, preferences, habits, and needs. Semantics of objects and scenes have traditionally been investigated for robotics in the perception, navigation and manipulation domain, but recent works have shown its benefits in an HRI context towards understanding and assisting human users. This workshop aims to explore ways to learn and ground abstract semantics of the physical world towards assistive autonomy.

Sonia Chernova (GT) and Matthew Gombolay (GT) assume the roles of General Chair and Local Chair, respectively, for the Conference on Robot Learning 2023. CORL stands as the foremost annual international conference that focuses on the intersection of robotics and machine learning.

Semantic Scene Understanding for Human-Robot Interaction

Maithili Patel, Fethiye Irmak Dogan, Zhen Zeng, Kim Baraka, Sonia Chernova

Service robots will be co-located with human users in an unstructured human-centered environment and will benefit from understanding the user’s daily activities, preferences, and needs towards fully assisting them. This workshop aims to explore how abstract semantic knowledge of the user’s environment can be used as a context in understanding and grounding information regarding the user’s instructions, preferences, habits, and needs. While object semantics have primarily been investigated for robotics in the perception and manipulation domain, recent works have shown the benefits of semantic modeling in a Human-Robot Interaction (HRI) context toward understanding and assisting human users. This workshop focuses on semantic information that can be useful in generalizing and interpreting user instructions, modeling user activities, anticipating user needs, and making the internal reasoning processes of a robot more interpretable to a user. Therefore, the workshop builds on topics from prior workshops such as Learning in HRI, behavior adaptation for assistance, and learning from humans and aims at facilitating cross-pollination across these domains through a common thread of utilizing abstract semantics of the physical world towards robot autonomy in assistive applications. We envision the workshop to touch on research areas such as unobtrusive learning from observations, preference learning, continual learning, enhancing the transparency of autonomous robot behavior, and user adaptation. The workshop aims to gather researchers working on these areas and provide fruitful discussions towards autonomous assistive robots that can learn and ground scene semantics for enhancing HRI.

Learn more: https://humanrobotinteraction.org/2023/ or visit https://humanrobotinteraction.org/2023/toc/

Showcase the world-class research, clinical, and training ecosystem of the a2 Collective as a platform for partnering with innovators to accelerate research and development in order to improve care and health outcomes for older Americans, including persons with Alzheimer’s disease and related dementias, and their caregivers.
Elizabeth Mynatt, Dean of Khoury College of Computer Sciences at Northeastern University and AI-CARING Co-PI, will be the keynote speaker.

Co-organized by Zhi Tan (GT), Sonia Chernova (GT), Jean Oh (CMU), Russell Perkins (UML), Paul Robinette (UML), Peter Schaldenbrand (CMU), Tanmay Shankar (CMU) and Diyi Yang (Stanford).

Recent advancements in AI and ML have enabled these technologies to enhance and improve our daily lives; however, these solutions are often based on simplified formulations and abstracted datasets that make them challenging to be applied in complex and personalized household domains. Furthermore, any household solution will require not only expertise across algorithmic AI but also experts in interaction, socio-technical issues, and problem space. Since the solutions touch on so many different fields, its research community is spread across different conferences. The workshop is designed to bring together interested AI experts who, while coming from different subfields, share the vision of using AI technologies to solve user problems at home.