Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Learning to walk through imitation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Regression-based online situation recognition for vehicular traffic scenarios
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Coupled dynamical system based arm-hand grasping model for learning fast adaptation strategies
Robotics and Autonomous Systems
Interactive imitation learning of object movement skills
Autonomous Robots
iProgram: intuitive programming of an industrial hri cell
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Learning probabilistic models for mobile manipulation robots
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper, we present an approach that allows a robot to observe, generalize, and reproduce tasks observed from multiple demonstrations. Motion capture data is recorded in which a human instructor manipulates a set of objects. In our approach, we learn relations between body parts of the demonstrator and objects in the scene. These relations result in a generalized task description. The problem of learning and reproducing human actions is formulated using a dynamic Bayesian network (DBN). The posteriors corresponding to the nodes of the DBN are estimated by observing objects in the scene and body parts of the demonstrator. To reproduce a task, we seek for the maximum-likelihood action sequence according to the DBN. We additionally show how further constraints can be incorporated online, for example, to robustly deal with unforeseen obstacles. Experiments carried out with a real 6-DoF robotic manipulator as well as in simulation show that our approach enables a robot to reproduce a task carried out by a human demonstrator. Our approach yields a high degree of generalization illustrated by performing a pick-and-place and a whiteboard cleaning task.