Call for Papers - ICRA 2026 Workshop: Semantics for Reliable Robot Autonomy: From Environment Understanding and Reasoning to Safe Interaction
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SiQi Zhou
Mar 11, 2026, 8:48:18 PM (2 days ago) Mar 11
to ai-robotics
Dear all,
We are delighted to announce our upcoming ICRA 2026 Workshop on Semantics for Reliable Robot Autonomy: From Environment Understanding and Reasoning to Safe Interaction, to be held in Vienna, Austria, on June 5, 2026, 8:50 - 17:30, with a relaxed get-together afterwards. We would like to invite you to participate and contribute your research in the form of short paper submissions.
Summary
Workshop Overview
Enabled by increasingly accessible robot hardware, robotic applications are undergoing a shift from specialized tasks in well-defined settings toward broader, general-purpose autonomy. In such domains, robots must not only perceive and localize within their environment but also interpret it semantically. This includes recognizing what is in the world as well as understanding the relevant properties of objects and reasoning about the implications of their actions (e.g., a warehouse robot must identify fragile items on the floor and infer that running over them could cause damage or create a safety hazard). Recent progress in large-scale models and language-conditioned learning opens new opportunities for robots to contextualize their actions, grounding them in common sense and semantic knowledge. Yet, leveraging these advances requires balancing prior knowledge and data-driven flexibility, designing appropriate environment representations to facilitate downstream tasks, and ensuring that semantic reasoning is reliably translated into safe behaviour. This workshop aims to foster interdisciplinary discussions across robot learning, perception, estimation, and control, while also drawing inspiration from linguistics and cognitive science. By bringing together these communities, the workshop will (i) explore methods that harness semantic understanding to advance reliable robot autonomy, and (ii) identify the opportunities and pressing challenges for real-world applications.
Call for Papers
We are inviting researchers from different disciplines to share novel ideas and ideas on topics pertinent to the workshop themes, which include but are not limited to:
The review process will be double-blind. Accepted papers will be published on the workshop webpage and presented as a spotlight talk or a poster. To recognize outstanding contributions, we will present an award for the best paper towards the end of the workshop, kindly sponsored by ORBBEC.
Paper Format
For any further questions, please contact: conta...@xcit.tum.de.
Speakers
Marco Pavone, Stanford University and Nvidia
Angel Chang, Simon Fraser University
Lukas Schmid, Massachusetts Institute of Technology
Janet Wiles, University of Queensland
Karol Hausman, Physical Intelligence (π)
George Pappas, University of Pennsylvania
Mengdi Xu, Tsinghua University
Dongheui Lee, TU Wien and German Aerospace Center
Ken Goldberg, UC Berkeley
Organizing Committee
Angela P. Schoellig, Technical University of Munich
Somil Bansal, Stanford University
SiQi Zhou, Simon Fraser University
Oier Mees, Microsoft
Lukas Brunke, University of Toronto
Benjamin Bogenberger, Technical University of Munich
Niklas Schlueter, Technical University of Munich
Haoming Zhang, Technical University of Munich
We are delighted to announce our upcoming ICRA 2026 Workshop on Semantics for Reliable Robot Autonomy: From Environment Understanding and Reasoning to Safe Interaction, to be held in Vienna, Austria, on June 5, 2026, 8:50 - 17:30, with a relaxed get-together afterwards. We would like to invite you to participate and contribute your research in the form of short paper submissions.
Summary
- Workshop Website: https://www.dynsyslab.org/icra2026-workshop-on-semantics-for-reliable-robot-autonomy
- Submission Page: http://tiny.cc/ICRA2026SRRA
- Initial Submission: April 1, 2026, 11:59 pm AoE
- Author Notification: May 15, 2026
Workshop Overview
Enabled by increasingly accessible robot hardware, robotic applications are undergoing a shift from specialized tasks in well-defined settings toward broader, general-purpose autonomy. In such domains, robots must not only perceive and localize within their environment but also interpret it semantically. This includes recognizing what is in the world as well as understanding the relevant properties of objects and reasoning about the implications of their actions (e.g., a warehouse robot must identify fragile items on the floor and infer that running over them could cause damage or create a safety hazard). Recent progress in large-scale models and language-conditioned learning opens new opportunities for robots to contextualize their actions, grounding them in common sense and semantic knowledge. Yet, leveraging these advances requires balancing prior knowledge and data-driven flexibility, designing appropriate environment representations to facilitate downstream tasks, and ensuring that semantic reasoning is reliably translated into safe behaviour. This workshop aims to foster interdisciplinary discussions across robot learning, perception, estimation, and control, while also drawing inspiration from linguistics and cognitive science. By bringing together these communities, the workshop will (i) explore methods that harness semantic understanding to advance reliable robot autonomy, and (ii) identify the opportunities and pressing challenges for real-world applications.
Call for Papers
We are inviting researchers from different disciplines to share novel ideas and ideas on topics pertinent to the workshop themes, which include but are not limited to:
- Perception methods incorporating semantic, geometric, and multi-modal information
- Efficient 3D object and environment representations from multi-modal sensor inputs
- Uncertainty estimation for 3D perception
- Contextual reasoning of the 3D environments
- Safe motion planning and control under semantic uncertainties
- Robot skill acquisition and learning leveraging semantic information
- Multi-agent collaboration through semantic information
- Foundation-model-based perception and decision-making methods
The review process will be double-blind. Accepted papers will be published on the workshop webpage and presented as a spotlight talk or a poster. To recognize outstanding contributions, we will present an award for the best paper towards the end of the workshop, kindly sponsored by ORBBEC.
Paper Format
- Suggested Length: minimum 2 and maximum 4 pages excluding references
- Style Template: https://ras.papercept.net/conferences/support/tex.php (ICRA paper template)
For any further questions, please contact: conta...@xcit.tum.de.
Speakers
Marco Pavone, Stanford University and Nvidia
Angel Chang, Simon Fraser University
Lukas Schmid, Massachusetts Institute of Technology
Janet Wiles, University of Queensland
Karol Hausman, Physical Intelligence (π)
George Pappas, University of Pennsylvania
Mengdi Xu, Tsinghua University
Dongheui Lee, TU Wien and German Aerospace Center
Ken Goldberg, UC Berkeley
Organizing Committee
Angela P. Schoellig, Technical University of Munich
Somil Bansal, Stanford University
SiQi Zhou, Simon Fraser University
Oier Mees, Microsoft
Lukas Brunke, University of Toronto
Benjamin Bogenberger, Technical University of Munich
Niklas Schlueter, Technical University of Munich
Haoming Zhang, Technical University of Munich
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