SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control Article Swipe
Arunkumar Byravan
,
Felix Leeb
,
Franziska Meier
,
Dieter Fox
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1710.00489
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1710.00489
In this work, we present an approach to deep visuomotor control using structured deep dynamics models. Our deep dynamics model, a variant of SE3-Nets, learns a low-dimensional pose embedding for visuomotor control via an encoder-decoder structure. Unlike prior work, our dynamics model is structured: given an input scene, our network explicitly learns to segment salient parts and predict their pose-embedding along with their motion modeled as a change in the pose space due to the applied actions. We train our model using a pair of point clouds separated by an action and show that given supervision only in the form of point-wise data associations between the frames our network is able to learn a meaningful segmentation of the scene along with consistent poses. We further show that our model can be used for closed-loop control directly in the learned low-dimensional pose space, where the actions are computed by minimizing error in the pose space using gradient-based methods, similar to traditional model-based control. We present results on controlling a Baxter robot from raw depth data in simulation and in the real world and compare against two baseline deep networks. Our method runs in real-time, achieves good prediction of scene dynamics and outperforms the baseline methods on multiple control runs. Video results can be found at: https://rse-lab.cs.washington.edu/se3-structured-deep-ctrl/
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1710.00489
- https://arxiv.org/pdf/1710.00489
- OA Status
- green
- Cited By
- 30
- References
- 18
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2763676071
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- OpenAlex ID
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https://openalex.org/W2763676071Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1710.00489Digital Object Identifier
- Title
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SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and ControlWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-10-02Full publication date if available
- Authors
-
Arunkumar Byravan, Felix Leeb, Franziska Meier, Dieter FoxList of authors in order
- Landing page
-
https://arxiv.org/abs/1710.00489Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1710.00489Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1710.00489Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Embedding, Computer vision, Segmentation, Baseline (sea), Space (punctuation), Encoder, Pose, Motion (physics), Block (permutation group theory), Deep learning, Point cloud, Mathematics, Geometry, Oceanography, Operating system, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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30Total citation count in OpenAlex
- Citations by year (recent)
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2021: 3, 2020: 11, 2019: 11, 2018: 5Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.two | 184 |
| abstract_inverted_index.via | 32 |
| abstract_inverted_index.able | 110 |
| abstract_inverted_index.data | 102, 173 |
| abstract_inverted_index.deep | 8, 13, 17, 186 |
| abstract_inverted_index.form | 99 |
| abstract_inverted_index.from | 170 |
| abstract_inverted_index.good | 194 |
| abstract_inverted_index.only | 96 |
| abstract_inverted_index.pair | 83 |
| abstract_inverted_index.pose | 27, 70, 140, 152 |
| abstract_inverted_index.real | 179 |
| abstract_inverted_index.runs | 190 |
| abstract_inverted_index.show | 92, 125 |
| abstract_inverted_index.that | 93, 126 |
| abstract_inverted_index.this | 1 |
| abstract_inverted_index.used | 131 |
| abstract_inverted_index.with | 61, 120 |
| abstract_inverted_index.Video | 208 |
| abstract_inverted_index.along | 60, 119 |
| abstract_inverted_index.depth | 172 |
| abstract_inverted_index.error | 149 |
| abstract_inverted_index.found | 212 |
| abstract_inverted_index.given | 44, 94 |
| abstract_inverted_index.input | 46 |
| abstract_inverted_index.learn | 112 |
| abstract_inverted_index.model | 41, 80, 128 |
| abstract_inverted_index.parts | 55 |
| abstract_inverted_index.point | 85 |
| abstract_inverted_index.prior | 37 |
| abstract_inverted_index.robot | 169 |
| abstract_inverted_index.runs. | 207 |
| abstract_inverted_index.scene | 118, 197 |
| abstract_inverted_index.space | 71, 153 |
| abstract_inverted_index.their | 58, 62 |
| abstract_inverted_index.train | 78 |
| abstract_inverted_index.using | 11, 81, 154 |
| abstract_inverted_index.where | 142 |
| abstract_inverted_index.work, | 2, 38 |
| abstract_inverted_index.world | 180 |
| abstract_inverted_index.Baxter | 168 |
| abstract_inverted_index.Unlike | 36 |
| abstract_inverted_index.action | 90 |
| abstract_inverted_index.change | 67 |
| abstract_inverted_index.clouds | 86 |
| abstract_inverted_index.frames | 106 |
| abstract_inverted_index.learns | 24, 51 |
| abstract_inverted_index.method | 189 |
| abstract_inverted_index.model, | 19 |
| abstract_inverted_index.motion | 63 |
| abstract_inverted_index.poses. | 122 |
| abstract_inverted_index.scene, | 47 |
| abstract_inverted_index.space, | 141 |
| abstract_inverted_index.actions | 144 |
| abstract_inverted_index.against | 183 |
| abstract_inverted_index.applied | 75 |
| abstract_inverted_index.between | 104 |
| abstract_inverted_index.compare | 182 |
| abstract_inverted_index.control | 10, 31, 134, 206 |
| abstract_inverted_index.further | 124 |
| abstract_inverted_index.learned | 138 |
| abstract_inverted_index.methods | 203 |
| abstract_inverted_index.modeled | 64 |
| abstract_inverted_index.models. | 15 |
| abstract_inverted_index.network | 49, 108 |
| abstract_inverted_index.predict | 57 |
| abstract_inverted_index.present | 4, 163 |
| abstract_inverted_index.results | 164, 209 |
| abstract_inverted_index.salient | 54 |
| abstract_inverted_index.segment | 53 |
| abstract_inverted_index.similar | 157 |
| abstract_inverted_index.variant | 21 |
| abstract_inverted_index.achieves | 193 |
| abstract_inverted_index.actions. | 76 |
| abstract_inverted_index.approach | 6 |
| abstract_inverted_index.baseline | 185, 202 |
| abstract_inverted_index.computed | 146 |
| abstract_inverted_index.control. | 161 |
| abstract_inverted_index.directly | 135 |
| abstract_inverted_index.dynamics | 14, 18, 40, 198 |
| abstract_inverted_index.methods, | 156 |
| abstract_inverted_index.multiple | 205 |
| abstract_inverted_index.SE3-Nets, | 23 |
| abstract_inverted_index.embedding | 28 |
| abstract_inverted_index.networks. | 187 |
| abstract_inverted_index.separated | 87 |
| abstract_inverted_index.consistent | 121 |
| abstract_inverted_index.explicitly | 50 |
| abstract_inverted_index.meaningful | 114 |
| abstract_inverted_index.minimizing | 148 |
| abstract_inverted_index.point-wise | 101 |
| abstract_inverted_index.prediction | 195 |
| abstract_inverted_index.real-time, | 192 |
| abstract_inverted_index.simulation | 175 |
| abstract_inverted_index.structure. | 35 |
| abstract_inverted_index.structured | 12 |
| abstract_inverted_index.visuomotor | 9, 30 |
| abstract_inverted_index.closed-loop | 133 |
| abstract_inverted_index.controlling | 166 |
| abstract_inverted_index.model-based | 160 |
| abstract_inverted_index.outperforms | 200 |
| abstract_inverted_index.structured: | 43 |
| abstract_inverted_index.supervision | 95 |
| abstract_inverted_index.traditional | 159 |
| abstract_inverted_index.associations | 103 |
| abstract_inverted_index.segmentation | 115 |
| abstract_inverted_index.gradient-based | 155 |
| abstract_inverted_index.pose-embedding | 59 |
| abstract_inverted_index.encoder-decoder | 34 |
| abstract_inverted_index.low-dimensional | 26, 139 |
| abstract_inverted_index.https://rse-lab.cs.washington.edu/se3-structured-deep-ctrl/ | 214 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |