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As a baseline, we use the non-linear controller that is part of the Crazyswarm using default
gains (“Mellinger”), where we observe an average Euclidean position error of 0.11m. A second
baseline is the same controller where we remove any computation that requires an additional state
(“Mellinger (no memory)”), i.e., we set the gains for the integral terms to zero. As expected, the
average position error increases – in this case to 0.2 m.
Our neural network with the motor settling time T = 0:15 has a mean position error of 0.19m,
which is similar to the hand-tuned baseline controller without memory. A network trained without
motor delays (T nearly zero) overshoots frequently and has a larger mean position error of 0.21m.
If the network is trained without sensor and motor noise, we measure a mean position error of
0.24m. The standard deviation of the norm of the position error for the neural networks is nearly
twice as high as for the non-linear feedback controller (0.06 and 0.11, respectively). Plots for some
controllers are shown in Fig. 4.3.
Note that none of our policies are explicitly trained for trajectory tracking. Nonetheless, they
still show competitive tracking performance compared to the baseline trajectory-tracking controller
specifically tuned for the Crazyflie.
4.5.3 Sim-to-Multi-Real: Learning without Model
We now investigate how well a single policy works across different quadrotor platforms. In all
cases, we quantify the hover quality as well as trajectory tracking using the metrics defined in
Section 4.4.5. For the medium quadrotor, we artificially limit the output RPM to 60% of its
maximum, to compensate for its higher thrust-to-weight ratio§. The results are shown in Table 4.4.
We use three different baselines to provide an estimate on achievable performance. The first
two baselines are identical to the Mellinger baselines in Section 4.5.2. A third baseline is used to
§Among all parameters the rough value of the thrust-to-weight ratio is relatively easy to estimate.
87
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 102 |
| Full text | As a baseline, we use the non-linear controller that is part of the Crazyswarm using default gains (“Mellinger”), where we observe an average Euclidean position error of 0.11m. A second baseline is the same controller where we remove any computation that requires an additional state (“Mellinger (no memory)”), i.e., we set the gains for the integral terms to zero. As expected, the average position error increases – in this case to 0.2 m. Our neural network with the motor settling time T = 0:15 has a mean position error of 0.19m, which is similar to the hand-tuned baseline controller without memory. A network trained without motor delays (T nearly zero) overshoots frequently and has a larger mean position error of 0.21m. If the network is trained without sensor and motor noise, we measure a mean position error of 0.24m. The standard deviation of the norm of the position error for the neural networks is nearly twice as high as for the non-linear feedback controller (0.06 and 0.11, respectively). Plots for some controllers are shown in Fig. 4.3. Note that none of our policies are explicitly trained for trajectory tracking. Nonetheless, they still show competitive tracking performance compared to the baseline trajectory-tracking controller specifically tuned for the Crazyflie. 4.5.3 Sim-to-Multi-Real: Learning without Model We now investigate how well a single policy works across different quadrotor platforms. In all cases, we quantify the hover quality as well as trajectory tracking using the metrics defined in Section 4.4.5. For the medium quadrotor, we artificially limit the output RPM to 60% of its maximum, to compensate for its higher thrust-to-weight ratio§. The results are shown in Table 4.4. We use three different baselines to provide an estimate on achievable performance. The first two baselines are identical to the Mellinger baselines in Section 4.5.2. A third baseline is used to §Among all parameters the rough value of the thrust-to-weight ratio is relatively easy to estimate. 87 |
