In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators Article Swipe
Dmytro Humeniuk
,
Houssem Ben Braiek
,
Thomas Reid
,
Foutse Khomh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3691620.3695281
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3691620.3695281
Testing autonomous robotic manipulators is challenging due to the complex software interactions between vision and control components. A crucial element of modern robotic manipulators is the deep learning based object detection model. The creation and assessment of this model requires real world data, which can be hard to label and collect, especially when the hardware setup is not available. The current techniques primarily focus on using synthetic data to train deep neural networks (DDNs) and identifying failures through offline or online simulation-based testing. However, the process of exploiting the identified failures to uncover design flaws early on, and leveraging the optimized DNN within the simulation to accelerate the engineering of the DNN for real-world tasks remains unclear. To address these challenges, we propose the MARTENS (Manipulator Robot Testing and Enhancement in Simulation) framework, which integrates a photorealistic NVIDIA Isaac Sim simulator with evolutionary search to identify critical scenarios aiming at improving the deep learning vision model and uncovering system design flaws. Evaluation of two industrial case studies demonstrated that MARTENS effectively reveals robotic manipulator system failures, detecting 25 % to 50 % more failures with greater diversity compared to random test generation. The model trained and repaired using the MARTENS approach achieved mean average precision (mAP) scores of 0.91 and 0.82 on real-world images with no prior retraining. Further fine-tuning on real-world images for a few epochs (less than 10) increased the mAP to 0.95 and 0.89 for the first and second use cases, respectively. In contrast, a model trained solely on real-world data achieved mAPs of 0.8 and 0.75 for use case 1 and use case 2 after more than 25 epochs.
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- Type
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- Language
- en
- Landing Page
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https://openalex.org/W4403520169Canonical identifier for this work in OpenAlex
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https://doi.org/10.1145/3691620.3695281Digital Object Identifier
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In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic ManipulatorsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-18Full publication date if available
- Authors
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Dmytro Humeniuk, Houssem Ben Braiek, Thomas Reid, Foutse KhomhList of authors in order
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https://doi.org/10.1145/3691620.3695281Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2410.19277Direct OA link when available
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Robot manipulator, Computer science, Artificial intelligence, Deep learning, Robot vision, Control engineering, Robot, Mobile robot, EngineeringTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.mAPs | 255 |
| abstract_inverted_index.mean | 202 |
| abstract_inverted_index.more | 182, 269 |
| abstract_inverted_index.real | 40 |
| abstract_inverted_index.test | 190 |
| abstract_inverted_index.than | 228, 270 |
| abstract_inverted_index.that | 168 |
| abstract_inverted_index.this | 37 |
| abstract_inverted_index.when | 52 |
| abstract_inverted_index.with | 141, 184, 214 |
| abstract_inverted_index.(less | 227 |
| abstract_inverted_index.(mAP) | 205 |
| abstract_inverted_index.Isaac | 138 |
| abstract_inverted_index.Robot | 126 |
| abstract_inverted_index.after | 268 |
| abstract_inverted_index.based | 28 |
| abstract_inverted_index.data, | 42 |
| abstract_inverted_index.early | 95 |
| abstract_inverted_index.first | 239 |
| abstract_inverted_index.flaws | 94 |
| abstract_inverted_index.focus | 63 |
| abstract_inverted_index.label | 48 |
| abstract_inverted_index.model | 38, 155, 193, 248 |
| abstract_inverted_index.prior | 216 |
| abstract_inverted_index.setup | 55 |
| abstract_inverted_index.tasks | 114 |
| abstract_inverted_index.these | 119 |
| abstract_inverted_index.train | 69 |
| abstract_inverted_index.using | 65, 197 |
| abstract_inverted_index.which | 43, 133 |
| abstract_inverted_index.world | 41 |
| abstract_inverted_index.(DDNs) | 73 |
| abstract_inverted_index.NVIDIA | 137 |
| abstract_inverted_index.aiming | 148 |
| abstract_inverted_index.cases, | 243 |
| abstract_inverted_index.design | 93, 159 |
| abstract_inverted_index.epochs | 226 |
| abstract_inverted_index.flaws. | 160 |
| abstract_inverted_index.images | 213, 222 |
| abstract_inverted_index.model. | 31 |
| abstract_inverted_index.modern | 21 |
| abstract_inverted_index.neural | 71 |
| abstract_inverted_index.object | 29 |
| abstract_inverted_index.online | 80 |
| abstract_inverted_index.random | 189 |
| abstract_inverted_index.scores | 206 |
| abstract_inverted_index.search | 143 |
| abstract_inverted_index.second | 241 |
| abstract_inverted_index.solely | 250 |
| abstract_inverted_index.system | 158, 174 |
| abstract_inverted_index.vision | 13, 154 |
| abstract_inverted_index.within | 102 |
| abstract_inverted_index.Further | 218 |
| abstract_inverted_index.MARTENS | 124, 169, 199 |
| abstract_inverted_index.Testing | 0, 127 |
| abstract_inverted_index.address | 118 |
| abstract_inverted_index.average | 203 |
| abstract_inverted_index.between | 12 |
| abstract_inverted_index.complex | 9 |
| abstract_inverted_index.control | 15 |
| abstract_inverted_index.crucial | 18 |
| abstract_inverted_index.current | 60 |
| abstract_inverted_index.element | 19 |
| abstract_inverted_index.epochs. | 272 |
| abstract_inverted_index.greater | 185 |
| abstract_inverted_index.offline | 78 |
| abstract_inverted_index.process | 85 |
| abstract_inverted_index.propose | 122 |
| abstract_inverted_index.remains | 115 |
| abstract_inverted_index.reveals | 171 |
| abstract_inverted_index.robotic | 2, 22, 172 |
| abstract_inverted_index.studies | 166 |
| abstract_inverted_index.through | 77 |
| abstract_inverted_index.trained | 194, 249 |
| abstract_inverted_index.uncover | 92 |
| abstract_inverted_index.However, | 83 |
| abstract_inverted_index.achieved | 201, 254 |
| abstract_inverted_index.approach | 200 |
| abstract_inverted_index.collect, | 50 |
| abstract_inverted_index.compared | 187 |
| abstract_inverted_index.creation | 33 |
| abstract_inverted_index.critical | 146 |
| abstract_inverted_index.failures | 76, 90, 183 |
| abstract_inverted_index.hardware | 54 |
| abstract_inverted_index.identify | 145 |
| abstract_inverted_index.learning | 27, 153 |
| abstract_inverted_index.networks | 72 |
| abstract_inverted_index.repaired | 196 |
| abstract_inverted_index.requires | 39 |
| abstract_inverted_index.software | 10 |
| abstract_inverted_index.testing. | 82 |
| abstract_inverted_index.unclear. | 116 |
| abstract_inverted_index.contrast, | 246 |
| abstract_inverted_index.detecting | 176 |
| abstract_inverted_index.detection | 30 |
| abstract_inverted_index.diversity | 186 |
| abstract_inverted_index.failures, | 175 |
| abstract_inverted_index.improving | 150 |
| abstract_inverted_index.increased | 230 |
| abstract_inverted_index.optimized | 100 |
| abstract_inverted_index.precision | 204 |
| abstract_inverted_index.primarily | 62 |
| abstract_inverted_index.scenarios | 147 |
| abstract_inverted_index.simulator | 140 |
| abstract_inverted_index.synthetic | 66 |
| abstract_inverted_index.Evaluation | 161 |
| abstract_inverted_index.accelerate | 106 |
| abstract_inverted_index.assessment | 35 |
| abstract_inverted_index.autonomous | 1 |
| abstract_inverted_index.available. | 58 |
| abstract_inverted_index.especially | 51 |
| abstract_inverted_index.exploiting | 87 |
| abstract_inverted_index.framework, | 132 |
| abstract_inverted_index.identified | 89 |
| abstract_inverted_index.industrial | 164 |
| abstract_inverted_index.integrates | 134 |
| abstract_inverted_index.leveraging | 98 |
| abstract_inverted_index.real-world | 113, 212, 221, 252 |
| abstract_inverted_index.simulation | 104 |
| abstract_inverted_index.techniques | 61 |
| abstract_inverted_index.uncovering | 157 |
| abstract_inverted_index.Enhancement | 129 |
| abstract_inverted_index.Simulation) | 131 |
| abstract_inverted_index.challenges, | 120 |
| abstract_inverted_index.challenging | 5 |
| abstract_inverted_index.components. | 16 |
| abstract_inverted_index.effectively | 170 |
| abstract_inverted_index.engineering | 108 |
| abstract_inverted_index.fine-tuning | 219 |
| abstract_inverted_index.generation. | 191 |
| abstract_inverted_index.identifying | 75 |
| abstract_inverted_index.manipulator | 173 |
| abstract_inverted_index.retraining. | 217 |
| abstract_inverted_index.(Manipulator | 125 |
| abstract_inverted_index.demonstrated | 167 |
| abstract_inverted_index.evolutionary | 142 |
| abstract_inverted_index.interactions | 11 |
| abstract_inverted_index.manipulators | 3, 23 |
| abstract_inverted_index.respectively. | 244 |
| abstract_inverted_index.photorealistic | 136 |
| abstract_inverted_index.simulation-based | 81 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5041261532 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I45683168 |
| citation_normalized_percentile.value | 0.72675173 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |