Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Robotics and Autonomous Systems
Using probabilistic reasoning over time to self-recognize
Robotics and Autonomous Systems
Learning Behaviors Models for Robot Execution Control
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Refining the execution of abstract actions with learned action models
Journal of Artificial Intelligence Research
Learning kinematic models for articulated objects
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Generality and legibility in mobile manipulation
Autonomous Robots
Bayesian network-based behavior control for Skilligent robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
An application of Lyapunov stability analysis to improve the performance of NARMAX models
Robotics and Autonomous Systems
Towards modelling complex robot training tasks through system identification
Robotics and Autonomous Systems
Motor simulation via coupled internal models using sequential Monte Carlo
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Embodied simulation based on autobiographical memory
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
Forward models applied in visual servoing for a reaching task in the iCub humanoid robot
Applied Bionics and Biomechanics
| Hi-index | 0.00 |
Forward models enable a robot to predict the effects of its actions on its own motor system and its environment. This is a vital aspect of intelligent behaviour, as the robot can use predictions to decide the best set of actions to achieve a goal. The ability to learn forward models enables robots to be more adaptable and autonomous; this paper describes a system whereby they can be learnt and represented as a Bayesian network. The robot's motor system is controlled and explored using 'motor babbling'. Feedback about its motor system comes from computer vision techniques requiring no prior information to perform tracking. The learnt forward model can be used by the robot to imitate human movement.