Workshops

Haptics plays an increasingly important role in human–robot interaction by enabling intuitive communication of physical information between humans and robotic systems. This workshop will bring together researchers working across haptics, robotics, and human–robot interaction to discuss emerging technologies, applications, and research challenges at their intersection. Topics will include wearable haptic interfaces, tactile and force feedback, haptic perception, robotic manipulation, and interactive robotic systems. Through invited talks and an open discussion, the workshop will highlight recent advances in haptic hardware, sensing, perception, and interaction, while exploring how these technologies can improve the safety, intuitiveness, and effectiveness of human–robot systems. The workshop will also provide a forum for identifying shared challenges and new opportunities for collaboration between the haptics and robotics communities.

This workshop will showcase a 14-degree-of-freedom hybrid robotic manufacturing cell integrating two 6-axis industrial robots with a 2-axis tilt-and-rotate positioning table. The system is designed to bring inspection, material removal, and material deposition into a single coordinated manufacturing environment.

Repair and remanufacturing of worn, damaged, or high-value industrial components often require multiple disconnected processes for inspection, material removal, and material restoration. Integrating these steps into a robotic workflow can reduce manual intervention, improve repeatability, and enable more adaptive repair strategies based on the actual geometry of each part.

At RISEx 2026, the workshop will demonstrate a workflow combining structured-light 3D scanning, robotic grinding, and wire arc additive manufacturing (WAAM). The process will begin with 3D scanning to capture the workpiece geometry and generate robot trajectories directly from the measured surface. A Yaskawa GP180 equipped with a grinding tool will perform controlled material removal, followed by a FANUC M-20iA carrying out WAAM on the prepared surface. An LJ Welding tilt-and-rotate positioning table will support workpiece orientation throughout the process.

The demonstration will highlight how these technologies can be integrated into a flexible robotic system for automated repair and remanufacturing of industrial components.

Exoskeleton technologies are increasingly being used in occupational ergonomics and human augmentation. This workshop introduces the fundamental principles and practical methods used to model and assess exoskeleton performance, usability, and real-world impact.

The workshop will begin by providing an overview of key evaluation approaches, including biomechanical, physiological, and user-centered assessment methods. Participants will explore commonly used outcome metrics. Through interactive demonstrations, they will learn how different evaluation metrics can be applied to compare exoskeleton designs and quantify their benefits and limitations. In addition, key findings from recent laboratory and in-field studies will be presented and discussed, highlighting both the benefits and limitations of current exoskeleton technologies in real-world work environments. Discussions will focus on challenges in translating laboratory findings into real-world deployment, personalization of assistance strategies, and factors influencing user acceptance and adoption. By combining foundational concepts with practical examples, this workshop aims to equip students, researchers, clinicians, ergonomics professionals, occupational health and safety practitioners, and industry professionals with the knowledge needed to critically evaluate exoskeleton technologies and contribute to their effective development and implementation. The workshop will conclude with a hands-on demonstration session, where participants will have the opportunity to experience and compare a variety of back-, neck-, shoulder-, and knee-support exoskeletons and discuss their performance, comfort, and usability.

This workshop will introduce students to PX4 (https://px4.io/), ROS 2 (https://www.ros.org/), and basic offboard drone control through a short presentation, a guided simulation exercise, and a real flight demonstration.

understand the basic PX4 flight-control architecture; identify common PX4 flight modes; understand basic ROS 2 communication with PX4; read vehicle-state information through ROS 2; generate simple trajectory references; implement a basic outer-loop controller; and test an offboard controller in SITL (software-in-the-loop) simulation and on a real vehicle.

Part 1 – Presentation: 40 minutes PX4 ecosystem, basic concepts ROS 2 basics: nodes, publishers, subscribers, topics, timing; communication between ROS 2 and PX4; coordinate frames; vehicle-state feedback and offboard commands; and structure of a simple outer-loop controller.

Part 2 – Simulation Hands-On Students will work with a prepared PX4 and ROS 2 simulation package. Vehicle-state subscription read the current vehicle position and velocity. Trajectory generation generate a simple reference such as a waypoint, square, or circle. Outer-loop control calculate a simple position-controller which feeds PX4 an acceleration command. Students will then run the completed node in simulation and observe the vehicle following the requested trajectory in Offboard mode.

Part 3 – Experimental Hands-On identify the main hardware components observe the transition to Offboard mode; run a verified version of the ROS 2 controller; and observe simple flight tasks such as hover, waypoint tracking, or a short trajectory.

Assistive and social assistive robots are emerging technologies that can supplement various functions for patients, such as improving social and communication skills, enabling them to live independently, and promoting their health and wellbeing. For care providers, these technologies can improve workflow efficiency and mitigate risks of musculoskeletal injuries. However, these technologies are also shifting how we deliver clinical care and interact with patients and members of the healthcare team.

While nurses have embraced digital health innovations such as electronic health records, they are less familiar with robotics and have limited opportunities to contribute to the design and evaluation of these technologies. Furthermore, the rapid development of artificial intelligence and automation and their applications in healthcare are presenting both opportunities and challenges to frontline care providers, including nurses. Engaging tech developers, researchers, emerging scholars, and clinicians in the co-design, evaluation, and strategic implementation of these emerging technologies is key to ensuring they support patient care needs and clinical workflows.

In collaboration with the Nursing Informatics Association of Alberta, this workshop offers an opportunity for participants to 1) learn about the prospects of robotics for advancing nursing practice and their potential implications, 2) gain a better understanding of knowledge gaps in this field, and 3) engage in an interactive dialogue with the presenters and fellow attendees to foster collaboration, innovation, and interdisciplinary research to advance knowledge and mentor the next generation of scholars.

Industrial tasks such as grinding, hammering, and surface finishing often depend on highly skilled human operators, making these processes difficult to standardize, scale, and automate. As industries move toward greater robotic automation, there is a growing need to capture and understand expert human performance so that these skills can be transferred to robotic systems.

This workshop at RISEx 2026 will demonstrate a surrogate human digital twinning framework for characterizing expert tool use. Handheld tools such as grinders and hammers will be instrumented with sensors including IMUs and RPM sensors to capture their dynamic behaviour during operation. Depth cameras, together with AprilTag-based tracking, will be used to estimate tool position and orientation and reconstruct its motion throughout the task. By combining these measurements, the system enables kinematic and dynamic profiling of human tool operation.

The resulting digital twin establishes a ground-truth baseline of expert human performance, including how the tool is moved and operated during the task. This benchmark can then support robot programming, trajectory generation, skill transfer, and learning-based robotic process development by providing a reference for how an experienced operator performs the same operation.

Machine learning models deployed in industrial plants often fail in ways validation performance gave no warning of. Two causes recur, neither the algorithm.

The first is coverage. Plants run to stay stable, so historical data clusters around normal operation. A model trained on that record learns a narrow band and nothing else. When the feedstock or ore body changes, it still returns confident values, with no sign it has left the conditions it knows.

The second is physics. Training sets rarely encode known constraints: mass and energy balances, physical bounds, one-directional relationships. A model can produce a value that fits the statistics and violates conservation, and no held-out split catches it.

Together they explain something students meet on entering the field: industry has grown skeptical of machine learning, for reasons in how models are trained and handed over.

The session runs as a fireside chat rather than a lecture. A host puts questions to researchers and practitioners about deployments that failed, experiments a plant declined to authorize, and results nobody questioned until it mattered. The audience joins for the final third, and each panelist closes with an open problem suitable as a thesis topic.

The material applies to robotics directly: a perception model outside its training distribution fails as a soft sensor does. The panel pairs NAIT applied researchers with industry practitioners who see these models after deployment, not only at publication.

This interactive workshop provides participants with a practical introduction to collaborative robotics through the Dobot Magician Lite educational robot platform. Working in small teams, participants will learn the fundamentals of robot programming, motion control, coordinate systems, end-effector operation, and task sequencing through guided hands-on exercises. After the training session, each team will apply their newly acquired skills in a friendly robot programming challenge that requires designing, programming, testing, and optimizing an autonomous robotic solution within a limited time. The workshop emphasizes experiential learning, teamwork, problem-solving, and rapid prototyping while introducing key concepts of modern industrial automation and Industry 4.0/5.0.