Evaluation of Generalization Performance of Visuo-Motor Learning by Analyzing Internal State Structured from Robot Motion

To provide robots with the flexibility they need to cope with various environments, motion generation techniques using deep learning have been proposed. Generalization in deep learning is expected to enable flexible processing in unknown situations and flexible motion generation. Motion generation models have been proposed to realize specific robot tasks, and their operation successes in unknown situations have been reported. However, their generalization performances have not been analyzed or verified in detail. In this paper, we analyze the internal state of a deep neural network using principal component analysis and verify the generalization of motion against environmental change, specifically a repositioned door knob. The results revealed that the motion primitives were structured in accordance with the position of the door knob. In addition, motion with high generalization performance was obtained by adaptive transition of motion primitives in accordance with changes in the door knob position. The robot was able to successfully perform a door-open-close task at various door knob positions.

[1]  Tetsuya Ogata,et al.  Put-In-Box task generated from multiple discrete tasks by humanoid robot using deep learning , 2017 .

[2]  Jun Tani,et al.  A Neurodynamic Account of Spontaneous Behaviour , 2011, PLoS Comput. Biol..

[3]  Giovanni De Magistris,et al.  Deep reinforcement learning for high precision assembly tasks , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[4]  Shigeki Sugano,et al.  Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning , 2017, IEEE Robotics and Automation Letters.

[5]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[6]  Tetsuya Ogata,et al.  Put-in-Box Task Generated from Multiple Discrete Tasks by aHumanoid Robot Using Deep Learning , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[9]  Sergey Levine,et al.  Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[11]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Martin Wattenberg,et al.  SmoothGrad: removing noise by adding noise , 2017, ArXiv.

[13]  Sergey Levine,et al.  End-to-End Training of Deep Visuomotor Policies , 2015, J. Mach. Learn. Res..

[14]  Jürgen Schmidhuber,et al.  Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.

[15]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[16]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[17]  Sergey Levine,et al.  Collective robot reinforcement learning with distributed asynchronous guided policy search , 2016, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[18]  Yuki Suga,et al.  Multimodal integration learning of robot behavior using deep neural networks , 2014, Robotics Auton. Syst..

[19]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[20]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[21]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[22]  Jun Tani,et al.  Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment , 2008, PLoS Comput. Biol..