Task modeling in imitation learning using latent variable models

An important challenge in robotic research is learning and reasoning about different manipulation tasks from scene observations.

[1]  Danica Kragic,et al.  Modeling and evaluation of human-to-robot mapping of grasps , 2009, 2009 International Conference on Advanced Robotics.

[2]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[3]  Danica Kragic,et al.  Towards Grasp-Oriented Visual Perception for Humanoid Robots , 2009, Int. J. Humanoid Robotics.

[4]  Trevor Darrell,et al.  Discriminative Gaussian process latent variable model for classification , 2007, ICML '07.

[5]  Sethu Vijayakumar,et al.  Synthesising Novel Movements through Latent Space Modulation of Scalable Control Policies , 2008, SAB.

[6]  Danica Kragic,et al.  Learning task constraints for robot grasping using graphical models , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Manuel Lopes,et al.  Learning Object Affordances: From Sensory--Motor Coordination to Imitation , 2008, IEEE Transactions on Robotics.

[8]  Marc Toussaint,et al.  Integrated motor control, planning, grasping and high-level reasoning in a blocks world using probabilistic inference , 2010, 2010 IEEE International Conference on Robotics and Automation.

[9]  Trevor Darrell,et al.  Factorized Orthogonal Latent Spaces , 2010, AISTATS.

[10]  K. Dautenhahn,et al.  Imitation and Social Learning in Robots, Humans and Animals: Behavioural, Social and Communicative Dimensions , 2009 .

[11]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[12]  C. Ek Shared Gaussian Process Latent Variables Models , 2009 .