GUESs: Generative modeling of Unknown Environments and Spatial Abstraction for Robots
Pages 1978 - 1980
Abstract
Representing unknown and missing knowledge about the environment is fundamental to leverage robot behavior and improve its performance in completing a task. However, reconstructing spatial knowledge beyond the sensory horizon of the robot is an extremely challenging task. Existing approaches assume that the environment static and features repetitive patterns (e.g. rectangular rooms) or that it can be all generalized with pre-trained models. Our goal is to remove such assumptions and to introduce a novel methodology that allows the robot to represent unknown spatial knowledge in dynamic and unstructured environments. To this end, we exploit generative learning to (1) learn a distribution of spatial landmarks observed during the robot mission and to (2) generate missing information in real-time. The proposed approach aims at supporting planning and decision-making processes needed for robot behaviors. In this paper, we describe architecture modeling the proposed approach and a first validation on a mobile platform.
References
[1]
Alireza Ahrary, Amir A.F. Nassiraei, and Masumi Ishikawa. 2007. A study of an autonomous mobile robot for a sewer inspection system. Artificial Life and Robotics 11, 1 (01 Jan 2007), 23--27. https://doi.org/10.1007/s10015-006-0392-x
[2]
S. Bai, J. Wang, F. Chen, and B. Englot. 2016. Information-theoretic exploration with Bayesian optimization. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 1816--1822. https://doi.org/10.1109/IROS.2016.7759289
[3]
H. J. Chang, C. S. G. Lee, Y. Lu, and Y. C. Hu. 2007. P-SLAM: Simultaneous Local-ization and Mapping With Environmental-Structure Prediction. IEEE Transactions on Robotics 23, 2 (April 2007), 281--293. https://doi.org/10.1109/TRO.2007.892230
[4]
Michael Jae-Yoon Chung*, Andrzej Pronobis*, Maya Cakmak, Dieter Fox, and Rajesh P. N. Rao. 2016. Autonomous Question Answering with Mobile Robots in Human-Populated Environments. In Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon, Korea. https://doi.org/10.1109/IROS.2016.7759146
[5]
Jiankang Deng, Shiyang Cheng, Niannan Xue, Yuxiang Zhou, and Stefanos Zafeiriou. 2018. UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6]
Felix Duvallet, Matthew R. Walter, Thomas Howard, Sachithra Hemachandra, Jean Oh, Seth Teller, Nicholas Roy, and Anthony Stentz. 2016. Inferring Maps and Behaviors from Natural Language Instructions. Springer International Publishing, 373--388. https://doi.org/10.1007/978-3-319-23778-7_25
[7]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 2672--2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
[8]
G. Grisetti, C. Stachniss, and W. Burgard. 2007. Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters. IEEE Transactions on Robotics 23, 1 (Feb 2007), 34--46. https://doi.org/10.1109/TRO.2006.889486
[9]
Kapil D. Katyal, Katie M. Popek, Chris Paxton, Joseph L. Moore, Kevin C. Wolfe, Philippe Burlina, and Gregory D. Hager. 2018. Occupancy Map Prediction Using Generative and Fully Convolutional Networks for Vehicle Navigation. CoRRabs/1803.02007 (2018). arXiv:1803.02007 http://arxiv.org/abs/1803.02007
[10]
Mohammad Al Khawaldah and Andreas Nüchter. 2015. Enhanced frontier-based exploration for indoor environment with multiple robots. Advanced Robotics 29, 10 (2015), 657--669. https://doi.org/10.1080/01691864.2015.1015443arXiv:https://doi.org/10.1080/01691864.2015.1015443
[11]
Diederik P. Kingma and Max Welling. 2013. Auto-Encoding Variational Bayes. CoRRabs/1312.6114 (2013).
[12]
Stefan Kohlbrecher, Johannes Meyer, Thorsten Graber, Karen Petersen, Uwe Klin-gauf, and Oskarvon Stryk. 2014. Hector Open Source Modules for Autonomous Mapping and Navigation with Rescue Robots. In Robo Cup 2013: Robot World Cup XVII, Sven Behnke, Manuela Veloso, Arnoud Visser, and Rong Xiong (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 624--631.
[13]
Raffaele Limosani, Raffele Esposito, Alessandro Manzi, Giancarlo Teti, Filippo Cavallo, and Paolo Dario. 2018. Robotic delivery service in combined outdoor in-door environments: technical analysis and user evaluation. Robotics and Autonomous Systems 103 (2018), 56--67. https://doi.org/10.1016/j.robot.2018.02.001
[14]
E. Nelson and N. Michael. 2015. Information-theoretic occupancy grid compression for high-speed information-based exploration. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4976--4982. https://doi.org/10.1109/IROS.2015.7354077
[15]
Tobias Neumann, Alexander Ferrein, Stephan Kallweit, and Ingrid Scholl. 2014.Towards a mobile mapping robot for underground mines. In Proc. of the 2014 PRASA, Rob Mech and AfLaI Int. Joint Symposium, Cape Town, South Africa.
[16]
S. Oßwald, M. Bennewitz, W. Burgard, and C. Stachniss. 2016. Speeding-Up Robot Exploration by Exploiting Background Information. IEEE Robotics and Automation Letters1, 2 (July 2016), 716--723. https://doi.org/10.1109/LRA.2016.2520560
[17]
K. Otsu, A. Agha-Mohammadi, and M. Paton. 2018. Where to Look? Predictive Per-ception With Applications to Planetary Exploration. IEEE Robotics and Automation Letters 3, 2 (April 2018), 635--642. https://doi.org/10.1109/LRA.2017.2777526
[18]
Andrzej Pronobis and Rajesh P. N. Rao. 2017. Learning Deep Generative Spatial Models for Mobile Robots. In Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vancouver, BC, Canada.https://doi.org/10.1109/IROS.2017.8202235 arXiv:cs.RO/1610.02627
[19]
Stéphane Ross, Geoffrey J Gordon, and Drew Bagnell. 2011. A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. In International Conference on Artificial Intelligence and Statistics. 627--635.
[20]
Cyrill Stachniss and Wolfram Burgard. 2003. Exploring Unknown Environments with Mobile Robots Using Coverage Maps. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI'03). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1127--1132. http://dl.acm.org/citation.cfm?id=1630659.1630821
[21]
Joan Vallvé and Juan Andrade-Cetto. 2015. Potential information fields for mobile robot exploration. Robotics and Autonomous Systems 69 (2015), 68--79. https://doi.org/10.1016/j.robot.2014.08.009 Selected papers from 6th European Conference on Mobile Robots.
Index Terms
- GUESs: Generative modeling of Unknown Environments and Spatial Abstraction for Robots
Recommendations
Acquiring Mobile Robot Behaviors by Learning Trajectory Velocities
The development of robots that learn from experience is a relentless challenge confronting artificial intelligence today. This paper describes a robot learning method which enables a mobile robot to simultaneously acquire the ability to avoid objects, ...
Autonomous Depth Perception of Humanoid Robot Using Binocular Vision System Through Sensorimotor Interaction with Environment
ICONIP 2015: Proceeings, Part II, of the 22nd International Conference on Neural Information Processing - Volume 9490In this paper, we explore how a humanoid robot having two cameras can learn to improve depth perception by itself. We propose an approach that can autonomously improve depth estimation of the humanoid robot. This approach can tune parameters that are ...
Learning of plan execution policies for indoor navigation
Special issue on KI-2001Most state-of-the-art navigation systems for autonomous service robots decompose navigation into global navigation planning and local (reactive) navigation. While the methods for navigation planning and local navigation themselves are well understood, ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2020
2289 pages
ISBN:9781450375184
- General Chairs:
- Amal El Fallah Seghrouchni,
- Gita Sukthankar,
- Program Chairs:
- Bo An,
- Neil Yorke-Smith Yorke-Smith
Sponsors
Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC
Publication History
Published: 13 May 2020
Check for updates
Author Tags
Qualifiers
- Extended-abstract
Conference
AAMAS '19
Sponsor:
AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems
May 9 - 13, 2020
Auckland, New Zealand
Acceptance Rates
Overall Acceptance Rate 1,155 of 5,036 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 34Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in