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environment we use Rg( ) = −d + 5~(d + 0:25) − SatS , where d is the distance from the center of
the creature’s torso to the goal g specified as a 2D Cartesian position. In contrast to the ReacherGoal
this environment has 33 § dimensional state space that describes Cartesian position, velocity and
orientation of the torso as well as angles and angular velocities of all eight joints. Note that in
both environments, the meta-network receives the goal information g as part of the state s in the
corresponding environments. Also, in practice, including the policy’s distribution parameters
directly in the meta-loss inputs, e.g. mean and standard deviation of a Gaussian policy, works
better than including the probability estimate (aSs), as it provides a more direct way to update
using back-propagation through the meta-loss.
Fig. 5.5 shows results for our tasks ¶ Fig. 5.5a and Fig. 5.5b show the results of the meta-test
time performance for the ReacherGoal and the AntGoal environments respectively. We can see
that ML3 loss significantly improves optimization speed in both scenarios compared to PPO. In
our experiments, we observed that on average ML3 requires 5 times fewer samples to reach 80%
of task performance in terms of our metrics for the model-free tasks.
To test the capability of the meta-loss to generalize across different architectures, we first
meta-trainM on an architecture with two layers and meta-test the same meta-loss on architectures
with varied number of layers. Fig. 5.5 (c+d) show meta-test time comparison for the ReacherGoal
and the AntGoal environments in a model-free setting for four different model architectures. Each
curve shows the average and the standard deviation over ten different tasks in each environment.
Our comparison clearly indicates that the meta-loss can be effectively re-used across multiple archi-tectures
with a mild variation in performance compare to the overall variance of the corresponding
task optimization.
§In contrast to the original Ant environment we remove external forces from the state.
¶Our framework is implemented using open-source libraries Higher [Grefenstette et al., 2019] for convenient
second-order derivative computations and Hydra [Yadan, 2019] for simplified handling of experiment configurations.
110
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 125 |
| Full text | environment we use Rg( ) = −d + 5~(d + 0:25) − SatS , where d is the distance from the center of the creature’s torso to the goal g specified as a 2D Cartesian position. In contrast to the ReacherGoal this environment has 33 § dimensional state space that describes Cartesian position, velocity and orientation of the torso as well as angles and angular velocities of all eight joints. Note that in both environments, the meta-network receives the goal information g as part of the state s in the corresponding environments. Also, in practice, including the policy’s distribution parameters directly in the meta-loss inputs, e.g. mean and standard deviation of a Gaussian policy, works better than including the probability estimate (aSs), as it provides a more direct way to update using back-propagation through the meta-loss. Fig. 5.5 shows results for our tasks ¶ Fig. 5.5a and Fig. 5.5b show the results of the meta-test time performance for the ReacherGoal and the AntGoal environments respectively. We can see that ML3 loss significantly improves optimization speed in both scenarios compared to PPO. In our experiments, we observed that on average ML3 requires 5 times fewer samples to reach 80% of task performance in terms of our metrics for the model-free tasks. To test the capability of the meta-loss to generalize across different architectures, we first meta-trainM on an architecture with two layers and meta-test the same meta-loss on architectures with varied number of layers. Fig. 5.5 (c+d) show meta-test time comparison for the ReacherGoal and the AntGoal environments in a model-free setting for four different model architectures. Each curve shows the average and the standard deviation over ten different tasks in each environment. Our comparison clearly indicates that the meta-loss can be effectively re-used across multiple archi-tectures with a mild variation in performance compare to the overall variance of the corresponding task optimization. §In contrast to the original Ant environment we remove external forces from the state. ¶Our framework is implemented using open-source libraries Higher [Grefenstette et al., 2019] for convenient second-order derivative computations and Hydra [Yadan, 2019] for simplified handling of experiment configurations. 110 |
