Pioneering the Future of Human Motion Prediction
Join us for the 8th Workshop on Long-term Human Motion Prediction, where experts from around the globe gather to share insights, innovative research, and cutting-edge developments in predicting human motion over extended periods. This workshop is a unique platform to explore how advancements in AI, robotics, and biomechanics can revolutionize human-computer interaction and safety.
Anticipating human motion is a key skill for intelligent systems that share a space or interact with humans. Accurate long-term predictions of human movement trajectories, body poses, actions or activities may significantly improve the ability of robots to plan ahead, anticipate the effects of their actions or to foresee hazardous situations. The topic has received increasing attention in recent years across several scientific communities with a growing spectrum of applications in service robots, self-driving cars, collaborative manipulators or tracking and surveillance. This workshop is the eight in a series of ICRA 2019-2026 events. The aim of this workshop is to bring together researchers and practitioners from different communities and to discuss recent developments in this field, promising approaches, their limitations, benchmarking techniques and open challenges.
Exploring the intersection of human behavior and robot navigation, join us to discuss the latest advancements in algorithms and applications that enhance the safety and efficiency of robots in dynamic, human-inhabited environments.
Focusing on the critical role of motion prediction in autonomous vehicles, we address the challenges of real-time decision-making and safety in self-driving technologies, fostering collaboration among researchers and practitioners to shape the future of automated driving.
Our workshop brings together experts in robotics and AI to explore how motion prediction can improve human-robot collaboration in industrial and service settings. Discover the latest research on anticipatory algorithms that enable robots to work alongside humans more safely and effectively.
Investigating the integration of multi-modal data in motion prediction, this workshop showcases cutting-edge research on combining visual, sensory, and contextual information to enhance robotic systems' predictive capabilities. Learn about the tools and techniques driving innovation in collaborative and autonomous robotics.
We welcome researchers in the field to submit papers to be presented in pitch-talks and as posters. Submitted manuscripts can be at most 4 pages (excluding references), formatted according to ICRA standards using the Paper Template downloadable on the IEEE ICRA 2026 website (two-column format). We encourage authors to additionally submit a video clip to complement their manuscript. Submissions will be reviewed and selected based on their originality, relevance to the workshop topics, contributions, technical clarity, and presentation.
We will accept submissions through CMT, as soon as the call for papers is opened.
We look forward to receiving your submissions!
We invite researchers to submit original contributions related to long-term human motion prediction. The papers will be presented in pitch-talks and as posters. Topics of interest include, but are not limited to:
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
Tim Schreiter, TUM
Andrey Rudenko, TUM
Luigi Palmieri, Robert Bosch GmbH
Boris Ivanovic, NVIDIA
Allan Wang, Miraikan - the National Museum of Emerging Science and Innovation
Dražen Brščić, Kyoto University
Luis Figueredo, University of Nottingham
Adriana Tapus, ENSTA, Institut Polytechnique de Paris
Achim J. Lilienthal, TUM/ORU
Will be announced soon.
Will be made available after the workshop.
