Projects

Phase 2 Projects

Robots exploring tools as extensions to their body autonomously (REBA+)


Project leaders: Tamim Asfour (Karlsruhe), Helge Ritter (Bielefeld), Robert Haschke (Bielefeld)

Researchers: Michael Bechtel (Karlsruhe), Qiang Li (Bielefeld)

Administration: Diana Becker (Karlsruhe), Susanne Strunk (Bielefeld)

Associates:

Summary:

The body scheme is increasingly appreciated as a core representation to organize interaction and learning of an embodied agent. While neuroscienti c research is unravelling a remarkable complexity of body schemes underlying the actions of biological agents, robotics 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 o er strong priors for rapid learning that in turn support a  exible 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, 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  exible coping with novel mechanical linkages between the body, the grasped tool and target objects.
As a major scienti c contribution to Autonomous Learning we aim to develop the current, largely kinematics focused body schema representations of robotics into stronger priors for learning and exploration by creating a rich body representation that addresses three key aspects of interaction learning learning:

  • enhancing the scope from the body morphology to a representation of body-tool-environment linkages
  • ˆenhancing the scope from a representation of morphology to a representation that includes controlˆ
  • enhancing the scope from minimal DOF systems to systems that o er 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 in robots acting in the real world.

Phase 1 Projects

Robots exploring tools as extensions to their body autonomously (REBA+)


Project leaders: Tamim Asfour (Karlsruhe), Helge Ritter (Bielefeld), Robert Haschke (Bielefeld)

Researchers: Michael Bechtel (Karlsruhe), Qiang Li (Bielefeld)

Administration: Diana Becker (Karlsruhe), Susanne Strunk (Bielefeld)

Associates:

Summary:

The body scheme is increasingly appreciated as a core representation to organize interaction and learning of an embodied agent. While neuroscienti c research is unravelling a remarkable complexity of body schemes underlying the actions of biological agents, robotics 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 o er strong priors for rapid learning that in turn support a  exible 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, 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  exible coping with novel mechanical linkages between the body, the grasped tool and target objects.
As a major scienti c contribution to Autonomous Learning we aim to develop the current, largely kinematics focused body schema representations of robotics into stronger priors for learning and exploration by creating a rich body representation that addresses three key aspects of interaction learning learning:

  • enhancing the scope from the body morphology to a representation of body-tool-environment linkages
  • ˆenhancing the scope from a representation of morphology to a representation that includes controlˆ
  • enhancing the scope from minimal DOF systems to systems that o er 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 in robots acting in the real world.