Motivated by the need of complex anthropomorphic robots to manage sophisticated spatial relationships between parts of their body and the environment, together with recent findings from the neurosciences about how the brain solves this challenge by means of a highly adaptable “body schema”, the present proposal pursues the goal of
We gradually increase our manual competence by exploring manual interaction spaces for many different kinds of objects. This is an active process that is very different from passive perception of "samples". The availability of humanoid robot hands offers the opportunity to investigate different strategies for such active exploration in realistic settings. In the present project, the investigation of such strategies shall be pursued from the perspective of „multimodal proprioception:“ correlating joint angles, partial contact information from touch sensors and joint torques as well as visual information about changes in finger and object position in such a way as to make predictions about "useful aspects" for shaping the ongoing interaction.
One central issue for the cognitive control of movement is the compensation of errors and learning processes that enhance error compensation mechanisms. This is especially true for very precise movements such as many manual actions.
The present project combines methods and conventional experimental settings (first order reality) with approaches from Virtual Reality and Augmented Reality to embed subjects in interaction loops in which the occurrence and perception of errors can be manipulated and studied in novel ways. In this way we hope to gain new clues about error correction mechanisms, error compensation learning and their replication in technical systems such as robots. read more »
What insights can we gain from psychological measurements of biomechanical parameters and subjective judgements of manual actions (like object grasping) about the structures of the underlying cognitive representations? In this project, we will bring together statistical methods (like structure dimensional and principal components analysis) with
connectionist approaches employing artificial neural networks to test different hypotheses about the cognitive structure of manual actions. A major goal will be to emulate and control grasping behavior for a broad range of objects in kinematic simulations and - as a longer term objective - in real physics on a robot platform. read more »
While language provides us with a concise code capturing much of the movement complexity of our mouth, we still lack a comparable representation for the movement of our hands. This project aims to create a database of human hand interaction patterns from a variety of multimodal data sources. An associated goal is to develop methods for the clustering of captured trajectory data into physics-based models of manual interaction. We hope that the resulting database can make a contribution towards a better grounding of control strategies for anthropomorphic robot hands and develop for robotics a similar utility as the WordNet database has for linguistics.
For a long time Brain-Computer Interfaces (BCI) had been destined to act as pur spelling devices which enabled paralyzed people to communicate by mere thought. Our current projects aim to extend the scope of these devices and develop novel techniques for brain-robot interaction. A successful application of BCIs to robotic devices will have the tremendous advantage that the users will not be limited to pure communication tasks but also be able to manipulate their surrounding directly by only imagining actions.
We gradually increase our manual competence by exploring manual interaction spaces for many different kinds of objects. This is an active process that is very different from passive perception of "samples". The availability of humanoid robot hands offers the opportunity to investigate different strategies for such active exploration in realistic settings. In the present project, the investigation of such strategies shall be pursued from the perspective of „multimodal proprioception:“ correlating joint angles, partial contact information from touch sensors and joint torques as well as visual information about changes in finger and object position in such a way as to make predictions about "useful aspects" for shaping the ongoing interaction.
While language provides us with a concise code capturing much of the movement complexity of our mouth, we still lack a comparable representation for the movement of our hands. This project aims to create a database of human hand interaction patterns from a variety of multimodal data sources. An associated goal is to develop methods for the clustering of captured trajectory data into physics-based models of manual interaction. We hope that the resulting database can make a contribution towards a better grounding of control strategies for anthropomorphic robot hands and develop for robotics a similar utility as the WordNet database has for linguistics.
For a long time Brain-Computer Interfaces (BCI) had been destined to act as pur spelling devices which enabled paralyzed people to communicate by mere thought. Our current projects aim to extend the scope of these devices and develop novel techniques for brain-robot interaction. A successful application of BCIs to robotic devices will have the tremendous advantage that the users will not be limited to pure communication tasks but also be able to manipulate their surrounding directly by only imagining actions.