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5.2.3 Shaping ML3 loss by adding extra loss information during meta-train
So far, we have discussed using standard task losses, such as MSE-loss for regression or reward
functions for RL settings. However, it is possible to provide more information about the task
at meta-train time, which can influence the learning of the loss-landscape. We can design our
task-losses to incorporate extra penalties; for instance we can extend the MSE-loss with Lextra and
weight the terms with and
:
LT = (y − f (x))2 +
Lextra (5.8)
In our work, we experiment with 4 different types of extra loss information at meta-train time:
for supervised learning we show that adding extra information through Lextra = ( − )2, where
are the optimal regression parameters, can help shape a convex loss-landscape for otherwise
non-convex optimization problems; we also show how we can use Lextra to induce a physics
prior in robot model learning. For reinforcement learning tasks we demonstrate that by providing
additional rewards in the task loss during meta-train time, we can encourage the trained meta-loss
to learn exploratory behaviors; and finally also for reinforcement learning tasks, we show how
expert demonstrations can be incorporated to learn loss functions which can generalize to new
tasks. In all settings, the additional information shapes the learned loss function such that the
environment does not need to provide this information during meta-test time.
5.3 Experiments
In this section we evaluate the applicability and the benefits of the learned meta-loss from two
different view points. First, we study the benefits of using standard task losses, such as the
104
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 119 |
| Full text | 5.2.3 Shaping ML3 loss by adding extra loss information during meta-train So far, we have discussed using standard task losses, such as MSE-loss for regression or reward functions for RL settings. However, it is possible to provide more information about the task at meta-train time, which can influence the learning of the loss-landscape. We can design our task-losses to incorporate extra penalties; for instance we can extend the MSE-loss with Lextra and weight the terms with and : LT = (y − f (x))2 + Lextra (5.8) In our work, we experiment with 4 different types of extra loss information at meta-train time: for supervised learning we show that adding extra information through Lextra = ( − )2, where are the optimal regression parameters, can help shape a convex loss-landscape for otherwise non-convex optimization problems; we also show how we can use Lextra to induce a physics prior in robot model learning. For reinforcement learning tasks we demonstrate that by providing additional rewards in the task loss during meta-train time, we can encourage the trained meta-loss to learn exploratory behaviors; and finally also for reinforcement learning tasks, we show how expert demonstrations can be incorporated to learn loss functions which can generalize to new tasks. In all settings, the additional information shapes the learned loss function such that the environment does not need to provide this information during meta-test time. 5.3 Experiments In this section we evaluate the applicability and the benefits of the learned meta-loss from two different view points. First, we study the benefits of using standard task losses, such as the 104 |
