Special Session
Special Session on
Hybrid Intelligence: Integrating Physics-Based Principles with Data-Driven AI -
HybAI
2026
26 - 28 October, 2026 - Angers, France
Within the 23rd International Conference on Informatics in Control, Automation and Robotics - ICINCO 2026
CO-CHAIRS
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Vittorio Lippi
Calejo Hybrid Intelligence AB
Germany
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Brief Bio
Dr. Vittorio Lippi holds a PhD in Robotics from the Scuola Superiore Sant'Anna in Pisa, Italy. His foundational academic background includes a BSc in Computer Engineering and an MSc in Industrial Automation from the Università di Pisa. Currently serving as an Artificial Intelligence Officer at Calejo Hybrid Intelligence AB, his work focuses on integrating machine learning, control theory, and simulation for complex real-time systems. He is concurrently a Lecturer and Teaching Fellow at the University College Freiburg. His specialized expertise bridges robotics—particularly humanoid posture control and human-robot interaction—with advanced statistical and deep learning methodologies.
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Ndivhuwo Makondo
IBM Research, University of the Witwatersrand
South Africa
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Brief Bio
Dr. Ndivhuwo Makondo is a Research Scientist and Manager in Data and AI at IBM Research - Africa, and a Visiting Researcher in Robotics at the School of Computer Science and Applied Mathematics, University of the Witwatersrand (Wits University). At Wits University, he is also a Research Associate at the Robotics Autonomous and Learning Lab (RAIL). At IBM Research, he leads a multidisciplinary team of scientists and engineers working on foundational and applied research at the intersection of knowledge representation, reasoning, learning, natural language processing, and data management. His work contributes to IBM’s global AI research strategy while localising advanced AI technologies through partnerships across the African continent. Previously, he led a sub-theme on learnable reasoning, with the vision to integrate domain knowledge, reasoning, and learning, to develop neural systems that incorporate various kinds of structured knowledge and formal reasoning for improved generalisation and reliability. Prior to joining IBM Research, Dr. Makondo worked as a Robotics Researcher at the Mobile Intelligent Autonomous Systems (MIAS), Council for Scientific and Industrial Research (CSIR), South Africa, leading a team developing vision and control algorithms for operating mobile manipulators in unstructured and dynamic environments, including manufacturing and mining. He has previously also worked on learning kinematic and dynamic models for low-level robot control.
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Leonard Johard
Calejo Hybrid Intelligence AB
Sweden
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Brief Bio
Leonard Johard received his PhD from SSSUP's PERCRO lab and has since been working with distributed brain simulators at CERN Openlab and been active as an inventor in the startup world. He co-founded computer vision retail analysis company Indivd AB and hybrid physics/AI intelligence startup Calejo Hybrid Intelligence AB.
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SCOPE
This special session focuses on the intersection of physical sciences, control engineering, and artificial intelligence. The scope encompasses the development, analysis, and application of gray-box modeling, approaches that strategically combine physical laws, domain knowledge, and first principles with the flexibility of modern data-driven AI. The session aims to bring together researchers and practitioners developing methods that are both intelligent and accountable, targeting safety-critical, data-scarce, or highly complex domains (such as industrial automation, healthcare, scientific discovery, and robotics) where purely black-box models are insufficient, and purely white-box models fail to scale.
TOPICS OF INTEREST
Topics of interest include, but are not limited to:
- Hybrid and Gray-Box Modeling
- Integration of physics-based principles, constraints, and domain knowledge with neural networks
- Physics-Informed Neural Networks (PINNs) for modeling, simulation, and control
- System Modeling, Control, and Digital Twins
- System identification and parameter estimation using hybrid models
- Controllers combining first-principles and data-driven components
- High-fidelity digital twins for optimization
- Applications in Industry, Medicine, and Science
- Predictive maintenance and process optimization
- Biomechanical models combined with AI for personalized medicine;
- Hybrid models for inverse problems in physics, chemistry, and climate science
- Human-Centric and Safety-Critical AI
- Human-in-the-loop systems
- Safety, robustness, and certification of AI systems where hallucinations or failures are unacceptable
- Explainability, Robustness, and Evaluation: Methods for interpretability, uncertainty quantification, and stability analysis in hybrid models
- Benchmarks for evaluating hybrid intelligence in real-world settings
IMPORTANT DATES
Paper Submission:
July 31, 2026
Authors Notification:
September 9, 2026
Camera Ready and Registration:
September 18, 2026
SPECIAL SESSION PROGRAM COMMITTEE
Available soon.
PAPER SUBMISSION
Prospective authors are invited to submit papers in any of the topics listed above.
Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
Please also check the Guidelines.
Papers must be submitted electronically via the web-based submission system using the appropriated button on this page.
PUBLICATIONS
After thorough reviewing by the special session program committee, all accepted papers will be published in a special section of the conference proceedings book - under an ISBN reference and on digital support - and submitted for indexation by SCOPUS, Google Scholar, DBLP, Semantic Scholar, EI and Web of Science / Conference Proceedings Citation Index.
SCITEPRESS is a member of CrossRef (http://www.crossref.org/) and every paper is given a DOI (Digital Object Identifier).
All papers presented at the conference venue will be available at the SCITEPRESS Digital Library