It is commonly assumed that the mind disposes of a repertoire of different strategies. The mechanisms underlying the selection of the proper strategy from the toolbox are still not well known. In this project we aim to better understand how strategy selection is cognitively and computationally implemented and how it can be improved.

Inspired by human grasping and manipulation capabilities, we build anthropomorphic soft robotic hands with a high degree of dexterity to enable robust interactions with the environment. We develop new sensor technologies that work with the highly compliant hands, while still providing useful sensor feedback. At the same time, we further increase the robustness of soft hands by devising control methods that reduce perceptual, model, and motion uncertainty through haptic feedback.

The "programming" of soft robots, however, remains largely an open problem. Whereas for hard robots, programming meant the specification of actuation commands, in the case of soft robots we must program control as well as "program" the robot's morphology to fully leverage the advantages afforded by soft material robotics. And, of course, these two problems interact very closely. We therefore speak of a co-design problem: we must determine control and morphology together to determine the behavior of a system.

We will develop a novel approach to Learning from Demonstration for teaching a robot complex, contact-rich manipulation tasks. This approach will enable the robots to physically operate complex locks and other mechanisms.

We investigate human and robot perception. The goal is to develop a constructive understanding of perceptual information processing, and develop "principles" based upon it.

The main obstacle to a wide-spread adoption of advanced manipulation systems in industry is their complexity, fragility, lack of strength, and difficulty of use. This project describes a path of disruptive innovation for the development of simple, compliant, yet strong, robust, and easy-to-program manipulation systems. The idea is: Soft Manipulation (SoMa).

In May 2015, our Team RBO won the Amazon Picking Challenge. This challenge addressed one of the last problems in warehouse automation: identifying and grasping objects from a cluttered warehouse shelf. Our robot was able win the competition by picking 10 out of 12 objects, outperforming 25 teams from Europe, USA and Asia.

We research how birds and robots can learn to solve complex kinematic problems.