Join us in person, or online for a talk by Lee St. James, Managing Director of the Urban Robotics Foundation. 

Virtual Meeting

Biography

Lee St. James serves as Managing Director of the Urban Robotics Foundation and an industry advisor to the ADVENTOR Program. URF is a global non-profit focused on preparing cities and technology companies for the safe deployment of autonomous mobile robots in public spaces. URF is the global lead for drafting ISO-4448 and ISO-25614. 

With a passion for understanding and optimizing human-robot interaction, Lee founded Social Robots in 2019 to address social isolation, loneliness, and boredom for older adults. She also works as Vice President for GlobalDWS- a technology solutions provider focused on enabling smart workplaces for mid-size organizations interested in deploying AI-enabled robotics, sensors and data analytic platforms. 

Designing Robots for Cities: From Autonomous Capability to Trustworthy Public Service

Autonomous robots and drones are slowly transitioning from controlled pilots to everyday infrastructure in North American cities- operating on sidewalks, on campuses, and in public facilities. Yet, as highlighted in URF's latest Whitepaper: "Robots in Public: Building the Governance Framework for Shared Human-Robots Spaces", the central challenge facing public-sector robotics is no longer technical feasibility, but governance, human-robot interaction, and public trust. 

This webinar introduces graduate-level designers and researchers to the reality of building public-facing robotic systems for municipal applications such as last-mile delivery, inspection, maintenance, mobility support, and public safety. Drawing on real-world case studies and regulatory analysis from across the globe, the session reframes robots and drones and participants in shared civic space, not just autonomous machines. 

Participants will explore why traditional engineering assumptions- trained users, opt-in interaction, and controlled environments- break down in public contexts, and how design decisions around legibility, predictability, accessibility, data practices, and accountability directly spare deployment outcomes. Special attention is given to lessons learned from fragmented regulation, reactive bans, and incidents involving sidewalk robots and robotaxis in U.S. and Canadian cities, including Toronto and San Francisco. 

This webinar equips emerging researchers and designers with a human-centered, governance-aware design lens, enabling them to build robotic systems that can scale responsibly within municipal environments while supporting innovation, equity, and public confidence. 

Have you ever seen a multi-drone show in the sky and wondered how they coordinate themselves? This type of question will be answered in our workshop, which will take place on the morning of May 11th.

Join us to hear from leading researchers who will break down how teams of robots coordinate, learn, and make decisions together in real time. From drone swarms to autonomous fleets, we’ll explore the ideas powering the next generation of robotics.

 What you’ll discover:

• How robot swarms achieve coordination—even with limited communication
• New research on using latent geometric spaces to create stable formations
• How learning algorithms can shape and optimize collective behavior
• A fresh perspective from mechanism design—using ideas from economics to make robots cooperate effectively
• Open challenges that researchers are tackling right now

Plus: free food will be provided.

Whether you’re into robotics, AI, control systems, or just curious about how complex systems work, this is a chance to see cutting-edge ideas.

The event is free for Ingenuity Labs and IEEE members. 

For more details and registration, follow the link: https://events.vtools.ieee.org/m/546171

Title: Food, freedom, and the quest for pilotless flight

Abstract: Transportation underpins many fundamental rights, from the human right to food security to the sovereign right to defend. For many of the world's remote areas including northern Canada, air transportation is the only year-round means for moving critical supplies—however, achieving fast, dependable, and affordable delivery remains a longstanding challenge. Autonomous aircraft have the potential to address this gap by simplifying operational constraints to high-frequency, high-reliability operations. In this talk, we will discuss the business case, concept of operations, regulatory considerations, and technical approach to developing and testing Ribbit's optionally piloted aircraft to date including key projects with Transport Canada, the National Research Council of Canada (NRC), and the Department of National Defence/Canadian Armed Forces (DND/CAF). We will also cover outstanding technical challenges moving forward, and comment on the current policy environment surrounding dual-use and defence modernization.

Bio: Dr. Jeremy Wang is an aerospace engineer and entrepreneur with over 10 years of experience in uncrewed and autonomous systems for advanced air mobility. Jeremy currently serves as President & COO of Ribbit, which he co-founded in 2020. Ribbit builds and operates dual-use self-flying airplanes to improve access to transportation, especially in rural, remote, and coastal regions. Ribbit is a contractor to multiple defence, research, and transportation agencies, and has partnered with wholesalers, Indigenous communities, and humanitarian groups who spend $73M annually on air freight. In 2023, Ribbit performed Canada’s first automated gate-to-gate flight. Jeremy is also active in the broader aerospace and defence community, serving as an adjunct professor at the University of Waterloo and Royal Military College ofCanada. Jeremy holds a BASc in Engineering Science (Toronto) and a PhD in Mechanical and Mechatronics Engineering (Waterloo).

Join us in person, or online for a talk by Dr. Stuart Reeves from the School of Computer Science at the University of Nottingham, UK. 

Virtual Meeting

Biography

Stuart Reeves is an Associate Professor at the School of Computer Science, University of Nottingham, UK. He develops hybrid approaches that blend Human-Computer Interaction ((HCI) research with studies grounded in ethnomethodology and conversation analysis to understand how digital technologies and infrastructures become praxeologically enmeshed within the organisation of everyday life. This research approach engages with critical technology design practices, and with conceptual implications for designers and developers of interactive systems. Recently Stuart's work has been preoccupied with examining 'real world' applications of Human-Robot Interaction (HRI) and Human-Robot Collaboration (e.g., Pelikan et al. 2024, Reeves et al. 2025, Reyes-Cruz et al. 2026), and what this means for robot designers via design frameworks (Pelikan et al. 2025). Stuart leads the Social Interaction and Technology (SIT) special interest group at Nottingham. 

Robots in Public: Understanding New Orders of Interaction

Recently we have seen robots being deployed in public places as fully operational commercial services (e.g., for delivery or transport). In this talk I want to discuss the human consequences of this 'at street level', and how it relates to the organisation of interaction in public and the local urban environment. Instead of focusing on the capabilities of robots themselves, I will examine how people- whether on foot, in cars, or pushing buggies- makes sense of and respond to robots as they encounter them when going about their everyday lives. On the basis of this, the talk will also explore what to make of such studies and how they can shape thinking about public robots and their design. 

Title:Cognitive Load in Complex Environments: Real-Time Measures, Stress Interactions, and Learning Outcomes

Abstract:
Cognitive Load Theory (CLT) describes the architecture of our cognition and provides guidance on how to apply this to the design of effective instructional materials and training. It has been used to develop many empirically validated methods for learning design in both educational and applied settings. Measuring CLT is an important tool towards the goal of creating well designed learning materials and online training systems. Many different methods have been used to measure load in real-time and these will be summarised in terms of those that have proven to be most useful within our research lab.  Recent research that looks at interactions between stress, CL and performance measures will also be discussed.  These findings have potential applications to the creation of real-time adaptative learning systems, which can adapt to individual differences (such as gender, stress and knowledge levels).  Finally, these adaptations will be discussed within the context of how real-time CL measurement could be applied to dynamic, complex, high-load environments, offering insights into practical implementation and future research directions.

Bio:
Dr Nadine Marcus is an Associate Professor in the School of Computer Science & Engineering, UNSW, Australia.  She co-leads the Computing & Education research group and teaches Human Computer Interaction. She has multidisciplinary research interests that includes the design of multimedia educational technology to improve human performance and learning; collection of empirical data to inform instructional design theories; and novel measures of cognitive load to inform interface design and create adaptive learning. She is an internationally recognised Cognitive Load Theory researcher who has been influential in the development of the Animation effect, Transient information effect and Embodied cognitive load related effects.  Her Educational Technology research led to the eLearning commercialization, Smartsparrow, which was acquired by Pearson Education. Her leadership in the field is evidenced by her extensive publications in highly ranked international peer reviewed journals and conferences, high citations and extensive funding within Educational Technology, Cognitive Psychology, Human Computer Interaction & Human Factors. partners.

TIME CHANGED to 1:30 pm

Title:
Perception solutions for enabling automated driving in winter conditions.

Abstract:
This talk presents methods and findings from research on perception solutions for automated driving in winter conditions, carried out at the Autonomy & Mobility Laboratory, Aalto University. Winter conditions pose several challenges for automated vehicle perception pipelines, which currently limit the applicability of the technology in adverse weather conditions. The talk focuses on two main research directions: denoising snowflakes from LiDAR data and road segmentation in snowy conditions. Airborne snowflakes introduce significant noise into LiDAR scans, which can hinder downstream perception tasks. To address this challenge, the talk presents deep learning approaches for point cloud denoising based on both supervised and self-supervised learning. In addition, snowy conditions drastically alter the visual appearance of the environment and the road, rendering road segmentation methods trained on traditional datasets unreliable. To overcome this, trajectory-based approaches leveraging vision foundation models are presented for learning varied road appearance without requiring manual labelling.

Bio:
Risto Ojala, DSc (Tech) is an Assistant Professor at Aalto University, Finland, where he leads the Autonomy & Mobility Laboratory within the Mechatronics research group. His research focuses on intelligent vehicles and mobile robotics, with particular emphasis on perception, sensor fusion, and applied machine learning for autonomous systems. He is also currently a Visiting Scholar at Simon Fraser University, Canada, collaborating with the Multi-Agent Robotic Systems Laboratory on research in semantic understanding for mobile robotics. His work develops perception solutions that enable robust autonomous operation in challenging environments. A central application of his research is automated driving in winter conditions, addressing problems such as road understanding, situational awareness, and perception reliability. His work has been published in leading robotics and intelligent transportation venues and he collaborates closely with both academic and industrial partners.

This is a Tune Up Race for our Canadian Roboracer teams ahead of the big event at ICRA in June.

Our annual Robotics and AI Symposium will be held again on Wednesday, October 14, 2026. 

Please check back again for more details as we get closer to the date.

Join us in person, or online for a talk by Dr. Joshua Woods from Ingenuity Labs, and the Department of Civil Engineering at Queen's University. 

Virtual Meeting

Biography

Dr. Joshua Woods, PhD PEng is an Assistant Professor in the Department of Civil Engineering at Queen's and the Mitchell Professor in Intelligent Infrastructure Monitoring at Ingenuity Labs. Dr. Wood's research is focused on safe, sustainable, and smart infrastructure. 

Next-Generation Infrastructure Inspection: Robots, Sensors, and AI in Action

The built environment is the backbone of our society and includes a broad range of infrastructure types that we rely on each and every day. For example, the roads and bridges that we drive on, the pipes that carry water to the tap, and even the building that you are reading this in right now! While we often take the day-to-day reliability of these structures for granted, they should be inspected on a regular basis and after extreme load events to ensure that they remain operational and safe. Traditional approaches to inspecting/ monitoring structures relies on human judgement, which can pose safety concerns and leads to questions about the reliability of the results. This talk will present the challenges surrounding inspection of civil engineering infrastructure and highlight two recent case studies on how autonomous vehicles and sensor systems could improve the quality of inspection data while reducing inspection time and costs. 

Title: How (formal) language can help AI agents learn, plan, and remember.

Abstract:

Humans have evolved languages over tens of thousands of years to provide useful abstractions for understanding and interacting with each other and with the physical world. Language comes in many forms. In Computer Science and in the study of AI, we have historically used knowledge representation languages and programming languages to capture our understanding of the world and to communicate unambiguously with computers. In this talk I will discuss how (formal) language can help AI agents learn, plan, and remember in the context of reinforcement learning. I’ll show how we can exploit the compositional syntax and semantics of formal language and automata to aid in the specification of complex reward-worthy behaviour, to improve the sample efficiency of learning, and to help agents learn what is necessary to remember. In doing so, I argue that (formal) language can help us address some of the challenges to reinforcement learning in the real world.

Bio:

Sheila McIlraith is a Professor in the Department of Computer Science at the University of Toronto, a Canada CIFAR AI Chair (Vector Institute), and an Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. McIlraith is the author of over 150 scholarly publications in the areas of knowledge representation, automated reasoning, and machine learning. Her work focuses on AI sequential decision making, broadly construed, through the lens of human-compatible AI. McIlraith is a fellow of the Association for Computing Machinery (ACM), a fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and a Schmidt Sciences AI2050 Senior Fellow. She and co-authors have been recognized with a number of honours for their scholarly contributions including the 2011 SWSA Ten-Year Award, the ICAPS 2022 Influential Paper Award, and the 2023 IJCAI-JAIR Best Paper Prize. She also catalyzed and continues to co-lead the University of Toronto Embedded Ethics Education Initiative (E3I).