Human prehension and dexterous robot hands
International Journal of Robotics Research
Bayesian inference of visual motion boundaries
Exploring artificial intelligence in the new millennium
Real-time classification of variable length multi-attribute motions
Knowledge and Information Systems
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
IEEE Transactions on Robotics
Programming-by-Demonstration of reaching motions-A next-state-planner approach
Robotics and Autonomous Systems
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In this paper, we address the problem of recognition of human grasps for five-fingered robotic hands and industrial robots in the context of programming-by-demonstration. The robot is instructed by a human operator wearing a data glove capturing the hand poses. For a number of human grasps, the corresponding fingertip trajectories are modeled in time and space by fuzzy clustering and Takagi-Sugeno (TS) modeling. This so-called time-clustering leads to grasp models using time as an input parameter and fingertip positions as outputs. For a sequence of grasps, the control system of the robot hand identifies the grasp segments, classifies the grasps and generates the sequence of grasps shown before. For this purpose, each grasp is correlated with a training sequence. By means of a hybrid fuzzy model, the demonstrated grasp sequence can be reconstructed.