Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots Article Swipe
Anusha Nagabandi
,
Guangzhao Yang
,
Thomas Asmar
,
Ravi Pandya
,
Gregory Kahn
,
Sergey Levine
,
Ronald S. Fearing
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1711.05253
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1711.05253
Millirobots are a promising robotic platform for many applications due to their small size and low manufacturing costs. Legged millirobots, in particular, can provide increased mobility in complex environments and improved scaling of obstacles. However, controlling these small, highly dynamic, and underactuated legged systems is difficult. Hand-engineered controllers can sometimes control these legged millirobots, but they have difficulties with dynamic maneuvers and complex terrains. We present an approach for controlling a real-world legged millirobot that is based on learned neural network models. Using less than 17 minutes of data, our method can learn a predictive model of the robot's dynamics that can enable effective gaits to be synthesized on the fly for following user-specified waypoints on a given terrain. Furthermore, by leveraging expressive, high-capacity neural network models, our approach allows for these predictions to be directly conditioned on camera images, endowing the robot with the ability to predict how different terrains might affect its dynamics. This enables sample-efficient and effective learning for locomotion of a dynamic legged millirobot on various terrains, including gravel, turf, carpet, and styrofoam. Experiment videos can be found at https://sites.google.com/view/imageconddyn
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1711.05253
- https://arxiv.org/pdf/1711.05253
- OA Status
- green
- Cited By
- 3
- References
- 38
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2949960092
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2949960092Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1711.05253Digital Object Identifier
- Title
-
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged MillirobotsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-11-14Full publication date if available
- Authors
-
Anusha Nagabandi, Guangzhao Yang, Thomas Asmar, Ravi Pandya, Gregory Kahn, Sergey Levine, Ronald S. FearingList of authors in order
- Landing page
-
https://arxiv.org/abs/1711.05253Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1711.05253Direct 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/1711.05253Direct OA link when available
- Concepts
-
Terrain, Computer science, Robot, Legged robot, Artificial intelligence, Artificial neural network, Underactuation, Control (management), Control engineering, Simulation, Engineering, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1, 2019: 2Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.how | 148 |
| abstract_inverted_index.its | 153 |
| abstract_inverted_index.low | 15 |
| abstract_inverted_index.our | 89, 127 |
| abstract_inverted_index.the | 97, 109, 141, 144 |
| abstract_inverted_index.This | 155 |
| abstract_inverted_index.have | 56 |
| abstract_inverted_index.less | 83 |
| abstract_inverted_index.many | 7 |
| abstract_inverted_index.size | 13 |
| abstract_inverted_index.than | 84 |
| abstract_inverted_index.that | 74, 100 |
| abstract_inverted_index.they | 55 |
| abstract_inverted_index.with | 58, 143 |
| abstract_inverted_index.Using | 82 |
| abstract_inverted_index.based | 76 |
| abstract_inverted_index.data, | 88 |
| abstract_inverted_index.found | 181 |
| abstract_inverted_index.gaits | 104 |
| abstract_inverted_index.given | 117 |
| abstract_inverted_index.learn | 92 |
| abstract_inverted_index.might | 151 |
| abstract_inverted_index.model | 95 |
| abstract_inverted_index.robot | 142 |
| abstract_inverted_index.small | 12 |
| abstract_inverted_index.their | 11 |
| abstract_inverted_index.these | 36, 51, 131 |
| abstract_inverted_index.turf, | 173 |
| abstract_inverted_index.Legged | 18 |
| abstract_inverted_index.affect | 152 |
| abstract_inverted_index.allows | 129 |
| abstract_inverted_index.camera | 138 |
| abstract_inverted_index.costs. | 17 |
| abstract_inverted_index.enable | 102 |
| abstract_inverted_index.highly | 38 |
| abstract_inverted_index.legged | 42, 52, 72, 166 |
| abstract_inverted_index.method | 90 |
| abstract_inverted_index.neural | 79, 124 |
| abstract_inverted_index.small, | 37 |
| abstract_inverted_index.videos | 178 |
| abstract_inverted_index.ability | 145 |
| abstract_inverted_index.carpet, | 174 |
| abstract_inverted_index.complex | 27, 62 |
| abstract_inverted_index.control | 50 |
| abstract_inverted_index.dynamic | 59, 165 |
| abstract_inverted_index.enables | 156 |
| abstract_inverted_index.gravel, | 172 |
| abstract_inverted_index.images, | 139 |
| abstract_inverted_index.learned | 78 |
| abstract_inverted_index.minutes | 86 |
| abstract_inverted_index.models, | 126 |
| abstract_inverted_index.models. | 81 |
| abstract_inverted_index.network | 80, 125 |
| abstract_inverted_index.predict | 147 |
| abstract_inverted_index.present | 65 |
| abstract_inverted_index.provide | 23 |
| abstract_inverted_index.robot's | 98 |
| abstract_inverted_index.robotic | 4 |
| abstract_inverted_index.scaling | 31 |
| abstract_inverted_index.systems | 43 |
| abstract_inverted_index.various | 169 |
| abstract_inverted_index.However, | 34 |
| abstract_inverted_index.approach | 67, 128 |
| abstract_inverted_index.directly | 135 |
| abstract_inverted_index.dynamic, | 39 |
| abstract_inverted_index.dynamics | 99 |
| abstract_inverted_index.endowing | 140 |
| abstract_inverted_index.improved | 30 |
| abstract_inverted_index.learning | 160 |
| abstract_inverted_index.mobility | 25 |
| abstract_inverted_index.platform | 5 |
| abstract_inverted_index.terrain. | 118 |
| abstract_inverted_index.terrains | 150 |
| abstract_inverted_index.different | 149 |
| abstract_inverted_index.dynamics. | 154 |
| abstract_inverted_index.effective | 103, 159 |
| abstract_inverted_index.following | 112 |
| abstract_inverted_index.including | 171 |
| abstract_inverted_index.increased | 24 |
| abstract_inverted_index.maneuvers | 60 |
| abstract_inverted_index.promising | 3 |
| abstract_inverted_index.sometimes | 49 |
| abstract_inverted_index.terrains, | 170 |
| abstract_inverted_index.terrains. | 63 |
| abstract_inverted_index.waypoints | 114 |
| abstract_inverted_index.Experiment | 177 |
| abstract_inverted_index.difficult. | 45 |
| abstract_inverted_index.leveraging | 121 |
| abstract_inverted_index.locomotion | 162 |
| abstract_inverted_index.millirobot | 73, 167 |
| abstract_inverted_index.obstacles. | 33 |
| abstract_inverted_index.predictive | 94 |
| abstract_inverted_index.real-world | 71 |
| abstract_inverted_index.styrofoam. | 176 |
| abstract_inverted_index.Millirobots | 0 |
| abstract_inverted_index.conditioned | 136 |
| abstract_inverted_index.controllers | 47 |
| abstract_inverted_index.controlling | 35, 69 |
| abstract_inverted_index.expressive, | 122 |
| abstract_inverted_index.particular, | 21 |
| abstract_inverted_index.predictions | 132 |
| abstract_inverted_index.synthesized | 107 |
| abstract_inverted_index.Furthermore, | 119 |
| abstract_inverted_index.applications | 8 |
| abstract_inverted_index.difficulties | 57 |
| abstract_inverted_index.environments | 28 |
| abstract_inverted_index.millirobots, | 19, 53 |
| abstract_inverted_index.high-capacity | 123 |
| abstract_inverted_index.manufacturing | 16 |
| abstract_inverted_index.underactuated | 41 |
| abstract_inverted_index.user-specified | 113 |
| abstract_inverted_index.Hand-engineered | 46 |
| abstract_inverted_index.sample-efficient | 157 |
| abstract_inverted_index.https://sites.google.com/view/imageconddyn | 183 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |