Page 147 |
Save page Remove page | Previous | 147 of 151 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
Robots and Systems (IROS), pages 5307–5314, 2015. doi: 10.1109/IROS.2015.7354126. URL
https://doi.org/10.1109/IROS.2015.7354126.
Andrew Y. Ng and Stuart J. Russell. Algorithms for inverse reinforcement learning. In Pat
Langley, editor, Proceedings of the Seventeenth International Conference on Machine Learning
(ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000, pages 663–670.
Morgan Kaufmann, 2000.
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves,
Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. Wavenet: A generative model for
raw audio. arXiv preprint arXiv:1609.03499, 2016.
Pierre-Yves Oudeyer and Fr´ed´eric Kaplan. What is intrinsic motivation? A typology of computa-tional
approaches. Front. Neurorobot., 2009, 2009. URL https://doi.org/10.3389/neuro.12.006.
2007.
Michael Ouimet and Jorge Cort´es. Coordinated rendezvous of underwater drifters in ocean
internal waves. In 53rd IEEE Conference on Decision and Control, CDC 2014, Los Angeles, CA,
USA, December 15-17, 2014, pages 6099–6104. IEEE, 2014. doi: 10.1109/CDC.2014.7040344.
URL https://doi.org/10.1109/CDC.2014.7040344.
Giambattista Parascandolo, Heikki Huttunen, and Tuomas Virtanen. Taming the waves: sine as
activation function in deep neural networks. 2017.
Xue Bin Peng, Marcin Andrychowicz,Wojciech Zaremba, and Pieter Abbeel. Sim-to-real transfer
of robotic control with dynamics randomization. In IEEE Intl Conf. on Robotics and Automation
(ICRA), pages 1–8, 2018. doi: 10.1109/ICRA.2018.8460528. URL https://doi.org/10.1109/ICRA.
2018.8460528.
Arvind Pereira, Jonathan Binney, Geoffrey A. Hollinger, and Gaurav S. Sukhatme. Risk-aware
path planning for autonomous underwater vehicles using predictive ocean models. J. Field
Robotics, 30(5):741–762, 2013. doi: 10.1002/rob.21472. URL https://doi.org/10.1002/rob.21472.
Anna Petrovskaya, Jaeheung Park, and Oussama Khatib. Probabilistic estimation of whole body
contacts for multi-contact robot control. In Proceedings 2007 IEEE International Conference on
Robotics and Automation, pages 568–573. IEEE, 2007.
Dean A Pomerleau. Efficient training of artificial neural networks for autonomous navigation.
Neural Computation, 3(1):88–97, 1991.
James A. Preiss, Wolfgang H¨onig, Gaurav S. Sukhatme, and Nora Ayanian. Crazyswarm: A
large nano-quadcopter swarm. In IEEE Intl Conf. on Robotics and Automation (ICRA), pages
3299–3304. IEEE, 2017. doi: 10.1109/ICRA.2017.7989376. URL https://doi.org/10.1109/ICRA.
2017.7989376.
132
Object Description
| Title | Data scarcity in robotics: leveraging structural priors and representation learning |
| Author | Molchanov, Artem |
| Author email | a.molchanov86@gmail.com;molchano@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2020-05-11 |
| Date submitted | 2020-08-11 |
| Date approved | 2020-08-11 |
| Restricted until | 2020-08-11 |
| Date published | 2020-08-11 |
| Advisor (committee chair) | Sukhatme, Gaurav Suhas |
| Advisor (committee member) |
Ayanian, Nora Culbertson, Heather Gupta, Satyandra K. |
| Abstract | Recent advances in Artificial Intelligence have benefited significantly from access to large pools of data accompanied in many cases by labels, ground truth values, or perfect demonstrations. In robotics, however, such data are scarce or absent completely. Overcoming this issue is a major barrier to move robots from structured laboratory settings to the unstructured real world. In this dissertation, by leveraging structural priors and representation learning, we provide several solutions when data required to operate robotics systems is scarce or absent. ❧ In the first part of this dissertation we study sensory feedback scarcity. We show how to use high-dimensional alternative sensory modalities to extract data when primary sensory sources are absent. In a robot grasping setting, we address the problem of contact localization and solve it using multi-modal tactile feedback as the alternative source of information. We leverage multiple tactile modalities provided by electrodes and hydro-acoustic sensors to structure the problem as spatio-temporal inference. We employ the representational power of neural networks to acquire the complex mapping between tactile sensors and the contact locations. We also investigate scarce feedback due to the high cost of measurements. We study this problem in a challenging field robotics setting where multiple severely underactuated aquatic vehicles need to be coordinated. We show how to leverage collaboration among the vehicles and the spatio-temporal smoothness of the ocean currents as a prior to densify feedback about ocean currents in order to acquire better controllability. ❧ In the second part of this dissertation, we investigate scarcity of the data related to the desired task. We develop a method to efficiently leverage simulated dynamics priors to perform sim-to-real transfer of a control policy when no data about the target system is available. We investigate this problem in the scenario of sim-to-real transfer of low-level stabilizing quadrotor control policies. We demonstrate that we can learn robust policies in simulation and transfer them to the real system while acquiring no samples from the real quadrotor. Finally, we consider the general problem of learning a model with a very limited number of samples using meta-learned losses. We show how such losses can encode a prior structure about families of tasks to create well-behaved loss landscapes for efficient model optimization. We demonstrate the efficiency of our approach for learning policies and dynamics models in multiple robotics settings. |
| Keyword | robotics; machine learning; artificial intelligence |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Molchanov, Artem |
| Physical access | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-MolchanovA-8923.pdf |
| Archival file | Volume13/etd-MolchanovA-8923.pdf |
Description
| Title | Page 147 |
| Full text | Robots and Systems (IROS), pages 5307–5314, 2015. doi: 10.1109/IROS.2015.7354126. URL https://doi.org/10.1109/IROS.2015.7354126. Andrew Y. Ng and Stuart J. Russell. Algorithms for inverse reinforcement learning. In Pat Langley, editor, Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000, pages 663–670. Morgan Kaufmann, 2000. Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499, 2016. Pierre-Yves Oudeyer and Fr´ed´eric Kaplan. What is intrinsic motivation? A typology of computa-tional approaches. Front. Neurorobot., 2009, 2009. URL https://doi.org/10.3389/neuro.12.006. 2007. Michael Ouimet and Jorge Cort´es. Coordinated rendezvous of underwater drifters in ocean internal waves. In 53rd IEEE Conference on Decision and Control, CDC 2014, Los Angeles, CA, USA, December 15-17, 2014, pages 6099–6104. IEEE, 2014. doi: 10.1109/CDC.2014.7040344. URL https://doi.org/10.1109/CDC.2014.7040344. Giambattista Parascandolo, Heikki Huttunen, and Tuomas Virtanen. Taming the waves: sine as activation function in deep neural networks. 2017. Xue Bin Peng, Marcin Andrychowicz,Wojciech Zaremba, and Pieter Abbeel. Sim-to-real transfer of robotic control with dynamics randomization. In IEEE Intl Conf. on Robotics and Automation (ICRA), pages 1–8, 2018. doi: 10.1109/ICRA.2018.8460528. URL https://doi.org/10.1109/ICRA. 2018.8460528. Arvind Pereira, Jonathan Binney, Geoffrey A. Hollinger, and Gaurav S. Sukhatme. Risk-aware path planning for autonomous underwater vehicles using predictive ocean models. J. Field Robotics, 30(5):741–762, 2013. doi: 10.1002/rob.21472. URL https://doi.org/10.1002/rob.21472. Anna Petrovskaya, Jaeheung Park, and Oussama Khatib. Probabilistic estimation of whole body contacts for multi-contact robot control. In Proceedings 2007 IEEE International Conference on Robotics and Automation, pages 568–573. IEEE, 2007. Dean A Pomerleau. Efficient training of artificial neural networks for autonomous navigation. Neural Computation, 3(1):88–97, 1991. James A. Preiss, Wolfgang H¨onig, Gaurav S. Sukhatme, and Nora Ayanian. Crazyswarm: A large nano-quadcopter swarm. In IEEE Intl Conf. on Robotics and Automation (ICRA), pages 3299–3304. IEEE, 2017. doi: 10.1109/ICRA.2017.7989376. URL https://doi.org/10.1109/ICRA. 2017.7989376. 132 |
