Robotics and Biological Behavior

Robots need to be able to understand and manipulate kinematic structures such as windows, door or drawers. We can draw inspiration from animals such as Goffin's cockatoos to teach robots these skills. Although these cockatoos certainly did not evolve to solve kinematic puzzles, they show remarkable success in such tasks. We want to find out how this is possible and how we can equip robots with similarly robust manipulation skills.

Robotic vision benefits from insights about human visual perception. But how about the other way around? Could robot visual perception help understand human visual perception better? Using a hierarchical functional architecture for synthetic perceptual systems, we study human performance and derive principles of robust information processing in perceptual systems. With this, we simultaneously advance our understanding of human vision and incorporate the underlying principles in robot vision.

We propose a computational principle for mapping sensory inputs to suitable actions, consisting of three building blocks: recursive estimators, interconnections, and differentiable programming. This integrated system can extract task-relevant information from the sensory input and generate suitable actions to achieve complex goals. We seek to study it as a model for different intelligent behaviors, thereby proposing it as a more general principle of intelligence.

This project aims to enhance complex, robust, and general robot manipulation learning through inductive biases based on structured regularities in the perception/action space. The biases will be hierarchical and composed of regularities at different levels of abstraction. They will be validated in a contact-rich manipulation task using a highly capable hand/arm system with multi-modal sensors, resulting in a powerful and data-efficient learning approach.

How do humans select the right strategy to solve a task? We aim to unlock the secrets of the mind's toolbox. In this project, we explore the mechanisms behind strategy selection in solving cognitive and behavioral tasks. Focusing on how the trade-off between accuracy and costs is inferred, we aim to provide a deeper understanding of the ecologically rational strategy selection process and how it can be improved.

We explore specialist and generalist approaches in artificial intelligence through the outfielder ball catching problem. Our analysis shows that these views lie on a spectrum, and the choice of problem representation is key. We find that, for this problem, the two views collapse to a single point on the spectrum. These findings have important implications for building smarter machines that can tackle complex decision-making problems more effectively.

The parrobots project was a seed funded project that we used to bootstrap our research and application for the project "Intelligent Kinematic Problem Solving". In this project we started our interdisciplinary cooperation to find out how Goffin's cockatoos can learn to solve mechanical puzzles. To this end we developed a novel experimental setup, the called the Modular Lockbox. It allows to set up new kinematic puzzles in a short time-frame, to quickly perform new experiments.

We study human grasping under a variety of conditions in order to identify and characterize different grasping strategies. Specially, we are interested in strategies that are robust to be performed under different kinds of impairment, e.g. visual. In addition to subjects' grasping with their natural hands, we also observe them when they use soft robotic hands such as RBO Hand 2 and Pisa/IIT Hand. Our final goal, is to transfer those robust strategies to a robot.