This work explores the use of formal methods to construct human-aware robot controllers to support the productivity requirements of humans. We tackle these types of scenarios via human workload-informed models and reactive synthesis. This strategy allows us to synthesize controllers that fulfill formal specifications that are expressed as linear temporal logic formulas.
Archives for February 2019
Here, we advance on real-time grasping pose estimation of single or multiple handles from RGB-D images, providing a speed up for assistive human-centered behaviors. We propose a versatile Bayesian framework that endows robots with the ability to infer various door kinematic models from observations of its motion. Combining this probabilistic approach with a state-of-the- art motion planner, we achieve efficient door grasping and subsequent door operation regardless of the kinematic model using the Toyota Human Support Robot.
arXiv Preprint: M. Arduengo, C. Torras, L. Sentis, A Versatile Framework for Robust and Adaptive Door Operation with a Mobile Manipulator Robot