A neural dynamics architecture for grasping that integrates perception and movement generation and enables on-line updating

We present a neural dynamics architecture for grasping that integrates perceptual processes of scene exploration, object selection and classification, and grasp pose estimation with motor processes such as planning and controlling reach and grasp movements. Inspired by theories of human embodied cognition, the entire architecture is essentially one big dynamical system from which discrete events such as initiating and terminating reaches and grasps emerge through dynamical instabilities. Using a Kinect sensor as input, we implement the architecture on a Kuka light weight arm with a Schunk Dextrous Hand and demonstrate grasping movements that are updated on-line when the object is shifted or rotated during movement planning or execution.