REBA

While neuroscientific research is unravelling a remarkable complexity and flexibility of body representations of biological agents during actions and the use of tools (Cardinali et al., 2012; Umilta et al., 2008; Maravita and Iriki, 2004), available approaches for representing body schemas for robots (Hoffmann et al., 2010a) still largely lack equally sophisticated, adaptive and dynamically extensible representations. Associated and largely open challenges are rich representations that marry body morphology, control, and the exploitation of redundant degrees of freedom in representations that offer strong priors for rapid learning that in turn support a flexible adaptation and extension of these representations to realize capabilities such as tool use or graceful degradation in case of malfunction of parts of the body tree. This motivates the present project: to develop, implement and evaluate for a robot rich extensions of its body schema, along with learning algorithms that use these representations as strong priors in order to enable rapid and autonomous usage of tools and a flexible coping with novel mechanical linkages between the body, the grasped tool and target objects. As a major scientific contribution to Autonomous Learning we aim to address these key aspects of interaction learning: ​

• ​enhancing the scope from the body morphology to a representation of body-tool-environment linkages
• enhancing the scope from a representation of morphology to a representation that includes control
• enhancing the scope from minimal DOF systems to systems that offer and exploit redundant degrees of freedom

We will develop these representations in the context of a systematically chosen ”matrix” of real-world interaction situations, arranged to pose learning challenges in increasing order of complexity along the above three dimensions. Thereby, we will build on the previous project, where we have developed a basic framework for adaptive body schemes emphasising the kinematics level. The project will thus directly contribute to enhance the autonomy of robots for adjusting their physical interaction with the world to the variety of situations that is characteristic of many natural environments. It will also advance the state of the art of representations that can support such capabilities, including representations that can autonomously extend themselves as a result of autonomous exploration.