To endow robots with autonomous behaviors in human populated environments, we require them to adapt to uncertain environments. This shift demands better perception, planning, and control systems, able to handle unpredictable interactions and noisy sensory feedback. In particular, being able to regulate the interaction forces becomes of paramount importance, ensuring precision or compliance according to the situation. Variable impedance control (VIC) offers a solution to the problem, regulating the stiffness based on perceived forces, robots can interact safely and avoid damage. However, manually coding these skills for every situation is impractical. The open question here is: which pieces of knowledge are needed for robots to encode these skills? Can the robot learn and re-use them in new situations?
In this direction, the research community presented different interesting approaches, which are based on exploratory behaviors or learning from human demonstration. In addition, recent AI methods, such as LLMs, demonstrated the capability to emulate human behaviors by means of natural interaction. Thus, why not exploit novel AI techniques to also regulate physical interactions in new contexts, by means of the combination of model-based and data-driven approaches? We believe that, enabled with such intelligence, robots can enforce safety and reliability, while building upon the principle of explainable AI.
The aim of this workshop is to bring together passionate researchers with different expertise, such as control, learning and AI, to examine current research on how to successfully transfer compliance knowledge enabling natural behaviors, and establish future research directions.
1. Foundations and Theoretical Advances in Variable Impedance Control
1.1- Fundamentals of Variable Impedance Control (VIC),
1.2- Learning and Optimal Control Applied to Physical Interaction,
1.3- VIC from Geometry Awareness and Manifold Learning Perspective,
1.4- Impedance Learning with Stable Dynamical Systems, and
1.5- Impedance Modulation with Stable Dynamical Systems.
2. Practical Applications and Technological Innovations
2.1- Physical Human-Robot Cooperation and Human Impedance,
2.2- Variable Impedance Strategies for Mobile Robots,
2.3- Variable Compliance with Soft Robots and Environments,
2.4- Benchmarking of Impedance Controllers, and
2.5- Innovative Applications of Variable Impedance Control.
3. Ethical, Societal Implications, and Future Horizons
3.1- Ethical and Societal Implications of Advanced Impedance Control,
3.2- Future Directions in Large Language Models (LLM)-Aided VIC,
3.3- LLM-Driven Adaptation in VIC for Human-Robot Interaction (HRI), and
3.4- Future Directions in Variable Impedance Learning and Control.
University of Delft, Netherlands
University of Sussex, UK
SINTEF, Norway
Technical University of Munich, Germany
Technical University of Munich, Germany
University of Tokyo, Japan
IEEE TC on Human-Robot Interaction & Coordination (RAS)
IEEE TC on Robot Learning (RAS)
IEEE TC on Model-Based Optimization for Robotics (RAS)
IEEE TC on Mobile Manipulation (RAS)
IEEE TC on Collaborative Automation for Flexible Manufacturing (RAS)
IEEE TC on System Identification and Adaptive Control (CSS) - Social Dinner Sponsor
IEEE TC on Manufacturing Automation and Robotic Control (CSS) - Awards Sponsor