Unlike most existing approaches to the grasp selection task for anthropomorphic robot hands, this vision-based project aims for a solution, which does not depend on an a-priori known 3D shape of the object. Instead it uses a decomposition of the object view (obtained from mono or stereo cameras) into local, grasping-relevant shape primitives, whose optimal grasp type and approach direction are known or learned beforehand. Based on this decomposition a list of possible grasps can be generated and ordered according to the anticipated overall grasp quality.
In the near future, more and more people will need assistance in everyday tasks while they still want to maintain a high degree of self-reliance. Cognitive robot servants will fulfill their individual needs. One promising way develop robots with a sufficiently high adaptability is to equip cognitive robots with task learning abilities, that lets them learn a task from demonstrations of naive (non-expert) users. This paradigm is widely known as Programming by Demonstration (PbD) or Imitation Learning.
Alignment is not restricted to support language, dialog functions and the execution of speech production. Similar mechanisms support the cooperative execution of more general actions. Besides the extension of alignment into the action domain, we hypothese that the formation of alignment is adaptive: Repetitions of actions facilitate the alignment and its adaptation is important for acquiring team expertise. In our opinion, adaptation and adaptive alignment paves the way for smooth and effective common acting.
Unlike most existing approaches to the grasp selection task for anthropomorphic robot hands, this vision-based project aims for a solution, which does not depend on an a-priori known 3D shape of the object. Instead it uses a decomposition of the object view (obtained from mono or stereo cameras) into local, grasping-relevant shape primitives, whose optimal grasp type and approach direction are known or learned beforehand. Based on this decomposition a list of possible grasps can be generated and ordered according to the anticipated overall grasp quality.
In the near future, more and more people will need assistance in everyday tasks while they still want to maintain a high degree of self-reliance. Cognitive robot servants will fulfill their individual needs. One promising way develop robots with a sufficiently high adaptability is to equip cognitive robots with task learning abilities, that lets them learn a task from demonstrations of naive (non-expert) users. This paradigm is widely known as Programming by Demonstration (PbD) or Imitation Learning.