Sensor fusion for robot control through deep reinforcement learning

Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information generated by multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world.

[1]  Steven Bohez,et al.  DIANNE: Distributed Artificial Neural Networks for the Internet of Things , 2015, M4IoT@Middleware.

[2]  Sergey Levine,et al.  Trust Region Policy Optimization , 2015, ICML.

[3]  Sergey Levine,et al.  Deep Reinforcement Learning for Robotic Manipulation , 2016, ArXiv.

[4]  Nitish Srivastava,et al.  Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..

[5]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[6]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[7]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[8]  Surya P. N. Singh,et al.  V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Honglak Lee,et al.  Deep learning for detecting robotic grasps , 2013, Int. J. Robotics Res..

[10]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[11]  Tom Schaul,et al.  Prioritized Experience Replay , 2015, ICLR.

[12]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[13]  Yuval Tassa,et al.  Learning Continuous Control Policies by Stochastic Value Gradients , 2015, NIPS.

[14]  Martin Vossiek,et al.  Multi-modal sensor fusion for indoor mobile robot pose estimation , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[15]  Clément Farabet,et al.  Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.

[16]  Hado van Hasselt,et al.  Double Q-learning , 2010, NIPS.

[17]  Gregory Shakhnarovich,et al.  FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.

[18]  Roland Siegwart,et al.  A robust and modular multi-sensor fusion approach applied to MAV navigation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Wolfram Burgard,et al.  Multimodal deep learning for robust RGB-D object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[20]  Veerachai Malyavej,et al.  Indoor robot localization by RSSI/IMU sensor fusion , 2013, 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[21]  Tucker R. Balch,et al.  Distributed sensor fusion for object position estimation by multi-robot systems , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[22]  Peter Kulchyski.  and , 2015 .

[23]  Yann LeCun,et al.  Deep belief net learning in a long-range vision system for autonomous off-road driving , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.