Learning a Single Near-hover Position Controller for Vastly Different Quadcopters Article Swipe
Dingqi Zhang
,
Antonio Loquercio
,
Xiangyu Wu
,
Ashish Kumar
,
Jitendra Malik
,
Mark W. Mueller
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.09232
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.09232
This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime. The core algorithmic idea is to learn a single policy that can adapt online at test time not only to the disturbances applied to the drone, but also to the robot dynamics and hardware in the same framework. We achieve this by training a neural network to estimate a latent representation of the robot and environment parameters, which is used to condition the behaviour of the controller, also represented as a neural network. We train both networks exclusively in simulation with the goal of flying the quadcopters to goal positions and avoiding crashes to the ground. We directly deploy the same controller trained in the simulation without any modifications on two quadcopters in the real world with differences in mass, size, motors, and propellers with mass differing by 4.5 times. In addition, we show rapid adaptation to sudden and large disturbances up to one-third of the mass of the quadcopters. We perform an extensive evaluation in both simulation and the physical world, where we outperform a state-of-the-art learning-based adaptive controller and a traditional PID controller specifically tuned to each platform individually. Video results can be found at https://youtu.be/U-c-LbTfvoA.
Related Topics
Concepts
Computer science
Controller (irrigation)
Robot
Adaptation (eye)
PID controller
Drone
Artificial neural network
Control theory (sociology)
Position (finance)
Artificial intelligence
Real-time computing
Control engineering
Control (management)
Engineering
Economics
Finance
Genetics
Agronomy
Temperature control
Physics
Optics
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2209.09232
- https://arxiv.org/pdf/2209.09232
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4296563123
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4296563123Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2209.09232Digital Object Identifier
- Title
-
Learning a Single Near-hover Position Controller for Vastly Different QuadcoptersWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-19Full publication date if available
- Authors
-
Dingqi Zhang, Antonio Loquercio, Xiangyu Wu, Ashish Kumar, Jitendra Malik, Mark W. MuellerList of authors in order
- Landing page
-
https://arxiv.org/abs/2209.09232Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2209.09232Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2209.09232Direct OA link when available
- Concepts
-
Computer science, Controller (irrigation), Robot, Adaptation (eye), PID controller, Drone, Artificial neural network, Control theory (sociology), Position (finance), Artificial intelligence, Real-time computing, Control engineering, Control (management), Engineering, Economics, Finance, Genetics, Agronomy, Temperature control, Physics, Optics, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.real | 148 |
| abstract_inverted_index.same | 70, 134 |
| abstract_inverted_index.show | 167 |
| abstract_inverted_index.size | 20 |
| abstract_inverted_index.test | 49 |
| abstract_inverted_index.that | 44 |
| abstract_inverted_index.this | 74 |
| abstract_inverted_index.time | 50 |
| abstract_inverted_index.used | 93 |
| abstract_inverted_index.very | 17 |
| abstract_inverted_index.with | 114, 150, 158 |
| abstract_inverted_index.Video | 215 |
| abstract_inverted_index.adapt | 46 |
| abstract_inverted_index.found | 219 |
| abstract_inverted_index.large | 173 |
| abstract_inverted_index.learn | 40 |
| abstract_inverted_index.mass, | 19, 153 |
| abstract_inverted_index.motor | 22 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.rapid | 27, 168 |
| abstract_inverted_index.robot | 64, 87 |
| abstract_inverted_index.shows | 26 |
| abstract_inverted_index.size, | 154 |
| abstract_inverted_index.train | 108 |
| abstract_inverted_index.tuned | 210 |
| abstract_inverted_index.where | 196 |
| abstract_inverted_index.which | 10, 91 |
| abstract_inverted_index.world | 149 |
| abstract_inverted_index.deploy | 132 |
| abstract_inverted_index.drone, | 59 |
| abstract_inverted_index.during | 32 |
| abstract_inverted_index.flying | 118 |
| abstract_inverted_index.latent | 83 |
| abstract_inverted_index.neural | 78, 105 |
| abstract_inverted_index.online | 47 |
| abstract_inverted_index.policy | 43 |
| abstract_inverted_index.single | 42 |
| abstract_inverted_index.sudden | 171 |
| abstract_inverted_index.times. | 163 |
| abstract_inverted_index.world, | 195 |
| abstract_inverted_index.achieve | 73 |
| abstract_inverted_index.applied | 56 |
| abstract_inverted_index.crashes | 126 |
| abstract_inverted_index.ground. | 129 |
| abstract_inverted_index.motors, | 155 |
| abstract_inverted_index.network | 79 |
| abstract_inverted_index.perform | 185 |
| abstract_inverted_index.results | 216 |
| abstract_inverted_index.trained | 136 |
| abstract_inverted_index.unknown | 30 |
| abstract_inverted_index.without | 140 |
| abstract_inverted_index.adaptive | 4, 202 |
| abstract_inverted_index.avoiding | 125 |
| abstract_inverted_index.deployed | 13 |
| abstract_inverted_index.directly | 131 |
| abstract_inverted_index.dynamics | 65 |
| abstract_inverted_index.estimate | 81 |
| abstract_inverted_index.hardware | 67 |
| abstract_inverted_index.network. | 106 |
| abstract_inverted_index.networks | 110 |
| abstract_inverted_index.physical | 194 |
| abstract_inverted_index.platform | 213 |
| abstract_inverted_index.position | 6 |
| abstract_inverted_index.proposes | 2 |
| abstract_inverted_index.runtime. | 33 |
| abstract_inverted_index.training | 76 |
| abstract_inverted_index.addition, | 165 |
| abstract_inverted_index.behaviour | 97 |
| abstract_inverted_index.condition | 95 |
| abstract_inverted_index.different | 18 |
| abstract_inverted_index.differing | 160 |
| abstract_inverted_index.extensive | 187 |
| abstract_inverted_index.one-third | 177 |
| abstract_inverted_index.positions | 123 |
| abstract_inverted_index.adaptation | 28, 169 |
| abstract_inverted_index.constants, | 23 |
| abstract_inverted_index.controller | 7, 135, 203, 208 |
| abstract_inverted_index.evaluation | 188 |
| abstract_inverted_index.framework. | 71 |
| abstract_inverted_index.near-hover | 5 |
| abstract_inverted_index.outperform | 198 |
| abstract_inverted_index.propellers | 157 |
| abstract_inverted_index.simulation | 113, 139, 191 |
| abstract_inverted_index.algorithmic | 36 |
| abstract_inverted_index.controller, | 100 |
| abstract_inverted_index.differences | 151 |
| abstract_inverted_index.environment | 89 |
| abstract_inverted_index.exclusively | 111 |
| abstract_inverted_index.parameters, | 90 |
| abstract_inverted_index.quadcopters | 15, 120, 145 |
| abstract_inverted_index.represented | 102 |
| abstract_inverted_index.traditional | 206 |
| abstract_inverted_index.disturbances | 31, 55, 174 |
| abstract_inverted_index.quadcopters, | 9 |
| abstract_inverted_index.quadcopters. | 183 |
| abstract_inverted_index.specifically | 209 |
| abstract_inverted_index.individually. | 214 |
| abstract_inverted_index.modifications | 142 |
| abstract_inverted_index.learning-based | 201 |
| abstract_inverted_index.representation | 84 |
| abstract_inverted_index.state-of-the-art | 200 |
| abstract_inverted_index.https://youtu.be/U-c-LbTfvoA. | 221 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |