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List of Figures
2.1 Cross-sectional schematic of the BioTac sensor (adapted from Su et al. [2012]). 18
2.2 The 18 objects used for data collection . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3 An example of a grasp used and contact samples collected during the experiment.
Left: an example grasp of an object. Right: an example of the detected contact
points and directions projected onto the zx plane. . . . . . . . . . . . . . . . . . . 23
2.4 The normalized histogram of the peak tapping forces applied to the object during
the experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.5 The tapping rod. Left: an image of the rod. Right: rod coordinate frame with the
locations of the markers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6 The wrist band with Vicon markers. Left: CAD model of the Barrett hand with
the band. Right: wrist coordinate frame with locations of the markers. . . . . . . 26
2.7 Results of regression using different sensor modalities for NN and GP regressors.
NN:Electr - NN with electrodes only; NN:Electr+PAC+PDC - NN with the
full set of sensor modalities (electrodes, PAC, PDC); GP:Electr+PAC+PDC
- GP with the full set of sensor modalities. x, y, z, yaw, pitch are results for
independent dimensions. xyz represents results for combined error, i.e. the norms
of the error vectors in Cartesian space. Errors in Cartesian space are reported in
cm; angular errors are reported in degrees. . . . . . . . . . . . . . . . . . . . . . . 27
2.8 Results of classification using different sets of sensor modalities: NN:Electr -
NN classifier with electrodes only; NN:Electr+PAC+PDC - NN classifier with
electrodes, PAC and PDC features; ST-HMP:Electr - ST-HMP feature learning
algorithm with SVM classifier applied to electrode features. . . . . . . . . . . . . 28
2.9 Results of NN classification using different time lengths of features, starting
from the moment contact event is detected. . . . . . . . . . . . . . . . . . . . . . 29
2.10 Results of NN classification using different grid steps. . . . . . . . . . . . . . . . 30
2.11 An example confusion matrix of the x dimension classification for one of the
objects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.1 Lagrangian drifters. Left: A prototype of our passive drifter system, which was
deployed in the Southern California Bight to measure ocean currents. Right: The
schematic shows the main components and the mode of operation of an active
drifter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
viii
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 8 |
| Full text | List of Figures 2.1 Cross-sectional schematic of the BioTac sensor (adapted from Su et al. [2012]). 18 2.2 The 18 objects used for data collection . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3 An example of a grasp used and contact samples collected during the experiment. Left: an example grasp of an object. Right: an example of the detected contact points and directions projected onto the zx plane. . . . . . . . . . . . . . . . . . . 23 2.4 The normalized histogram of the peak tapping forces applied to the object during the experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.5 The tapping rod. Left: an image of the rod. Right: rod coordinate frame with the locations of the markers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.6 The wrist band with Vicon markers. Left: CAD model of the Barrett hand with the band. Right: wrist coordinate frame with locations of the markers. . . . . . . 26 2.7 Results of regression using different sensor modalities for NN and GP regressors. NN:Electr - NN with electrodes only; NN:Electr+PAC+PDC - NN with the full set of sensor modalities (electrodes, PAC, PDC); GP:Electr+PAC+PDC - GP with the full set of sensor modalities. x, y, z, yaw, pitch are results for independent dimensions. xyz represents results for combined error, i.e. the norms of the error vectors in Cartesian space. Errors in Cartesian space are reported in cm; angular errors are reported in degrees. . . . . . . . . . . . . . . . . . . . . . . 27 2.8 Results of classification using different sets of sensor modalities: NN:Electr - NN classifier with electrodes only; NN:Electr+PAC+PDC - NN classifier with electrodes, PAC and PDC features; ST-HMP:Electr - ST-HMP feature learning algorithm with SVM classifier applied to electrode features. . . . . . . . . . . . . 28 2.9 Results of NN classification using different time lengths of features, starting from the moment contact event is detected. . . . . . . . . . . . . . . . . . . . . . 29 2.10 Results of NN classification using different grid steps. . . . . . . . . . . . . . . . 30 2.11 An example confusion matrix of the x dimension classification for one of the objects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.1 Lagrangian drifters. Left: A prototype of our passive drifter system, which was deployed in the Southern California Bight to measure ocean currents. Right: The schematic shows the main components and the mode of operation of an active drifter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 viii |
