World ITF World is a conference that brings 2000 technology experts together. We were present with our work on robotic manipulation of challenging objects.

What we demonstrated

At ITF World we demonstrated robotic unfolding with a multi-robotarm setup. Our work was also on national television!

What we offer

We prove the applicability and feasibility of collaborative robots in your business. We help companies with tailored, collaborative robot setups in which we automate tasks with hard-to-handle objects in dynamic environments. We bring our experience and knowledge of our own research and the state of the art to the industry. We do this by providing explorative studies and developing proof-of-concept collaborative robot setups that come with customer-specific software and documentation in the context of your business.

We can provide a study or proof-of-context of robotic manipulation in the context of your business.

In automation, specific machines are built to automate a very specific task within narrowly defined boundaries. With the collaborative robots of our cloth folding demo, we can move those boundaries and broaden the scope. We focus on collaborative robot solutions for businesses that perform (1) heterogeneous tasks with (2) heterogeneous objects in (3) heterogeneous environments by learning task execution. This form of learned manipulation allows businesses to save costs and relieve employees from repetitious tasks and let them focus on value-added activities requiring a human operator.

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From cloth folding to your business

Dexterous robotic manipulation skills will increase global productivity by means of automating repetitive tasks. However, current robots require a predictable and controlled environment to perform tasks, which limits their ability to be used in dynamic environments with a wide range of objects and human activity. Deformable objects, such as cloth, present an additional challenge because they are hard to perceive and manipulate. Our research is centered on allowing robots to acquire human-like manipulation skills autonomously in a data-efficient manner. We focus on supplying rich training signals by exploiting prior knowledge embedded in human-task solving and system modeling. Our robotic applications focus on difficult to perceive and handle object such as cloth, plastics, and reflective objects.