Neural network perception for mobile robot guidance
Neural network perception for mobile robot guidance
Artificial Intelligence Review - Special issue on lazy learning
The haptic display of complex graphical environments
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Six degree-of-freedom haptic rendering using voxel sampling
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Haptics Issues in Virtual Environments
CGI '00 Proceedings of the International Conference on Computer Graphics
LAMA: a language for automatic mechanical assembly
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
A robot learning from demonstration framework to perform force-based manipulation tasks
Intelligent Service Robotics
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This paper explores the feasibility of reconstructing human manipulation skills in complex constrained motion by tracing and learning the manipulation performed by the operator. The peg-in-hole insertion problem is used as a case study, which represents a typical constrained motion force sensitive manufacturing task with the attendant issues of jamming, tight clearance and the need for quick assembly times. In the developed system, position and contact force and torque as well as orientation data generated in the haptic rendered virtual environment combined with a priori knowledge about the task are used to identify and learn the skills in the newly demonstrated task. The recorded training data is classified into contact states, which are identified with hidden Markov model (HMM) as human skills. The HMM parameters are obtained from the training data. By evaluating the controller's performance in each contact state from haptic rendered virtual environment, the robot develops the best trajectories to imitate the human behaviour. In this paper the significance of this research project is highlighted and the developed approach and the progress made so far on this project are reported.