Learning to Navigate: Exploiting Deep Networks to Inform Sample-Based Planning During Vision-Based Navigation

Recent applications of deep learning to navigation have generated end-to-end navigation solutions whereby visual sensor input is mapped to control signals or to motion primitives. The resulting visual navigation strategies work very well at collision avoidance and have performance that matches traditional reactive navigation algorithms while operating in real-time. It is accepted that these solutions cannot provide the same level of performance as a global planner. However, it is less clear how such end-to-end systems should be integrated into a full navigation pipeline. We evaluate the typical end-to-end solution within a full navigation pipeline in order to expose its weaknesses. Doing so illuminates how to better integrate deep learning methods into the navigation pipeline. In particular, we show that they are an efficient means to provide informed samples for sample-based planners. Controlled simulations with comparison against traditional planners show that the number of samples can be reduced by an order of magnitude while preserving navigation performance. Implementation on a mobile robot matches the simulated performance outcomes.

[1]  Ali Farhadi,et al.  Target-driven visual navigation in indoor scenes using deep reinforcement learning , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Torsten Bertram,et al.  Efficient trajectory optimization using a sparse model , 2013, 2013 European Conference on Mobile Robots.

[3]  Sergey Levine,et al.  Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Roland Siegwart,et al.  From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[6]  Jürgen Schmidhuber,et al.  A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots , 2016, IEEE Robotics and Automation Letters.

[7]  Charles Richter,et al.  Safe Visual Navigation via Deep Learning and Novelty Detection , 2017, Robotics: Science and Systems.

[8]  Jan Peters,et al.  Stable reinforcement learning with autoencoders for tactile and visual data , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[9]  Sergey Levine,et al.  (CAD)$^2$RL: Real Single-Image Flight without a Single Real Image , 2016, Robotics: Science and Systems.

[10]  Jiajun Wu,et al.  Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning , 2015, NIPS.

[11]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Patricio A. Vela,et al.  PiPS: Planning in perception space , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Jianxiong Xiao,et al.  DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Razvan Pascanu,et al.  Learning to Navigate in Complex Environments , 2016, ICLR.

[15]  Shaohua Li,et al.  Autonomous exploration of mobile robots through deep neural networks , 2017 .

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

[17]  Martial Hebert,et al.  Introspective perception: Learning to predict failures in vision systems , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[18]  Ashutosh Saxena,et al.  High speed obstacle avoidance using monocular vision and reinforcement learning , 2005, ICML.

[19]  Wojciech Jaskowski,et al.  ViZDoom: A Doom-based AI research platform for visual reinforcement learning , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

[20]  Martial Hebert,et al.  Learning robust failure response for autonomous vision based flight , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Marlos C. Machado,et al.  State of the Art Control of Atari Games Using Shallow Reinforcement Learning , 2015, AAMAS.

[22]  Martial Hebert,et al.  Learning monocular reactive UAV control in cluttered natural environments , 2012, 2013 IEEE International Conference on Robotics and Automation.

[23]  K. Madhava Krishna,et al.  Autonomous navigation of generic monocular quadcopter in natural environment , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[24]  Honglak Lee,et al.  Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning , 2014, NIPS.

[25]  Guido C. H. E. de Croon,et al.  Self-supervised monocular distance learning on a lightweight micro air vehicle , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[26]  Oussama Khatib,et al.  Elastic bands: connecting path planning and control , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[27]  Ali Farhadi,et al.  Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).