Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAE

In this study, we propose a method to model the local and global features of the drawing/grinding trajectory with hierarchical Variational Autoencoders (VAEs). By combining two separately trained VAE models in a hierarchical structure, it is possible to generate trajectories with high reproducibility for both local and global features. The hierarchical generation network enables the generation of higher-order trajectories with a relatively small amount of training data. The simulation and experimental results demonstrate the generalization performance of the proposed method. In addition, we confirmed that it is possible to generate new trajectories, which have never been learned in the past, by changing the combination of the learned models.

[1]  Stefan Schaal,et al.  Computational approaches to motor learning by imitation. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[2]  Tetsuya Ogata,et al.  Developmental Human-Robot Imitation Learning of Drawing with a Neuro Dynamical System , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[3]  Yoshua Bengio,et al.  Drawing and Recognizing Chinese Characters with Recurrent Neural Network , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Miki Haseyama,et al.  Face Synthesis via User Manipulation of Disentangled Latent Representation , 2020, 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE).

[5]  Nan Jiang,et al.  Hierarchical Imitation and Reinforcement Learning , 2018, ICML.

[6]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[7]  Alex Lascarides,et al.  Interpretable Latent Spaces for Learning from Demonstration , 2018, CoRL.

[8]  Stefan Schaal,et al.  Learning from Demonstration , 1996, NIPS.

[9]  Geir Hovland,et al.  Skill acquisition from human demonstration using a hidden Markov model , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[10]  Kouhei Ohnishi,et al.  Motion control for advanced mechatronics , 1996 .

[11]  Ales Ude,et al.  Acquisition of Elementary Robot Skills from Human Demonstration , 1995 .

[12]  Toshiaki Tsuji,et al.  Trajectory adjustment for nonprehensile manipulation using latent space of trained sequence-to-sequence model* , 2019, Adv. Robotics.

[13]  Gianluca Cerminara,et al.  Anomaly Detection with Conditional Variational Autoencoders , 2019, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA).

[14]  Yiannis Demiris,et al.  Hierarchical behavioral repertoires with unsupervised descriptors , 2018, GECCO.

[15]  Manuela M. Veloso,et al.  Confidence-based policy learning from demonstration using Gaussian mixture models , 2007, AAMAS '07.

[16]  Joshua B. Tenenbaum,et al.  Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation , 2016, NIPS.

[17]  Douglas Eck,et al.  A Neural Representation of Sketch Drawings , 2017, ICLR.

[18]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[19]  Silvio Savarese,et al.  Neural Task Programming: Learning to Generalize Across Hierarchical Tasks , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Toshiaki Tsuji,et al.  A Cooking Support System with Force Visualization Using Force Sensors and an RGB-D Camera , 2016, AsiaHaptics.

[21]  Christopher N. Anderson,et al.  Transformer VAE: A Hierarchical Model for Structure-Aware and Interpretable Music Representation Learning , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[22]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.