Robots are increasingly collaborating with humans, making the need for safe interaction and robustness in unforeseen situations vital. For instance, surgery robots are required to safely interact with both professionals and patients in complex procedures. While the scope in this case is well-defined, there are applications that additionally require robustness with regard to unforeseen situations. A self-driving car, for example, is expected to appropriately react to novel traffic scenarios and unexpected human behavior. Safety and robustness become particularly challenging when advanced machine learning algorithms are used in the entire robot control loop - from perception and prediction to planning and control. Despite the rigorous training in controlled lab settings and extensive testing across diverse scenarios, there is a known risk coming with the unpredictability of their real-world deployment.

Given the wide-reaching implications of robot deployment, there is a pressing need for research into methods that ensure safety and robustness across all circumstances. This workshop aims to foster collaboration between researchers and practitioners with regard to three related research areas: Data Curation, Generalization, and Validation and Verification.

You would like to participate? Great! Please register for the CoRL workshop day here. We are looking forward meeting you!