The integration of robots into our daily lives depends on their ability to operate reliably and safely in complex, dynamic environments. Despite significant progress, achieving robust performance under uncertainty remains a major challenge. Real-time robust planning is computationally demanding, particularly for tasks such as whole-body control of multi-body robots or handling hybrid dynamics in contact-based interactions.
An essential component of this challenge is accurately considering the effects of uncertainties on future plans. However, despite considerable advancements, efficient uncertainty representation and propagation remain immature for fast, real-time control of complex robots in general real-world tasks.
This workshop aims to address these challenges by bringing together top researchers and practitioners to explore the state of the art in integrated robust planning and control in the presence of uncertainties. We will focus on planning strategies that ensure system functionality despite disturbances and uncertainties, aligning with the growing trend toward optimization-based predictive control.
The workshop will cover a range of methodologies that balance formality and practicality while addressing uncertainties in tasks involving motion, manipulation, and interaction. Our goal is to showcase the best our community has to offer in enhancing the practical applicability of robots in uncertain real-world scenarios.
