Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation Article Swipe
Kun-Ru Wu
,
Ning Liu
,
Zhen Zhao
,
Di Qiu
,
Jinming Li
,
Zhengping Che
,
Zhiyuan Xu
,
Qinru Qiu
,
Jian Tang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.08957
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.08957
Improving data utilization, especially for imperfect data from task failures, is crucial for robotic manipulation due to the challenging, time-consuming, and expensive data collection process in the real world. Current imitation learning (IL) typically discards imperfect data, focusing solely on successful expert data. While reinforcement learning (RL) can learn from explorations and failures, the sim2real gap and its reliance on dense reward and online exploration make it difficult to apply effectively in real-world scenarios. In this work, we aim to conquer the challenge of leveraging imperfect data without the need for reward information to improve the model performance for robotic manipulation in an offline manner. Specifically, we introduce a Self-Supervised Data Filtering framework (SSDF) that combines expert and imperfect data to compute quality scores for failed trajectory segments. High-quality segments from the failed data are used to expand the training dataset. Then, the enhanced dataset can be used with any downstream policy learning method for robotic manipulation tasks. Extensive experiments on the ManiSkill2 benchmark built on the high-fidelity Sapien simulator and real-world robotic manipulation tasks using the Franka robot arm demonstrated that the SSDF can accurately expand the training dataset with high-quality imperfect data and improve the success rates for all robotic manipulation tasks.
Related Topics
Concepts
Computer science
Imperfect
Transformer
Fidelity
Artificial intelligence
Benchmark (surveying)
Robot
Imitation
Similarity (geometry)
Machine learning
Human–computer interaction
Engineering
Philosophy
Image (mathematics)
Social psychology
Electrical engineering
Voltage
Psychology
Linguistics
Geodesy
Geography
Telecommunications
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.08957
- https://arxiv.org/pdf/2401.08957
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391013206
All OpenAlex metadata
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https://openalex.org/W4391013206Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.08957Digital Object Identifier
- Title
-
Learning from Imperfect Demonstrations with Self-Supervision for Robotic ManipulationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-01-17Full publication date if available
- Authors
-
Kun-Ru Wu, Ning Liu, Zhen Zhao, Di Qiu, Jinming Li, Zhengping Che, Zhiyuan Xu, Qinru Qiu, Jian TangList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.08957Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.08957Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2401.08957Direct OA link when available
- Concepts
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Computer science, Imperfect, Transformer, Fidelity, Artificial intelligence, Benchmark (surveying), Robot, Imitation, Similarity (geometry), Machine learning, Human–computer interaction, Engineering, Philosophy, Image (mathematics), Social psychology, Electrical engineering, Voltage, Psychology, Linguistics, Geodesy, Geography, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 17, 26, 53, 81, 88, 95, 131, 138, 142, 161, 166, 176, 182, 187, 196 |
| abstract_inverted_index.(IL) | 32 |
| abstract_inverted_index.(RL) | 46 |
| abstract_inverted_index.Data | 110 |
| abstract_inverted_index.SSDF | 183 |
| abstract_inverted_index.data | 1, 6, 22, 86, 119, 133, 193 |
| abstract_inverted_index.from | 7, 49, 130 |
| abstract_inverted_index.make | 65 |
| abstract_inverted_index.need | 89 |
| abstract_inverted_index.real | 27 |
| abstract_inverted_index.task | 8 |
| abstract_inverted_index.that | 114, 181 |
| abstract_inverted_index.this | 75 |
| abstract_inverted_index.used | 135, 147 |
| abstract_inverted_index.with | 148, 190 |
| abstract_inverted_index.Then, | 141 |
| abstract_inverted_index.While | 43 |
| abstract_inverted_index.apply | 69 |
| abstract_inverted_index.built | 164 |
| abstract_inverted_index.data, | 36 |
| abstract_inverted_index.data. | 42 |
| abstract_inverted_index.dense | 60 |
| abstract_inverted_index.learn | 48 |
| abstract_inverted_index.model | 96 |
| abstract_inverted_index.rates | 198 |
| abstract_inverted_index.robot | 178 |
| abstract_inverted_index.tasks | 174 |
| abstract_inverted_index.using | 175 |
| abstract_inverted_index.work, | 76 |
| abstract_inverted_index.(SSDF) | 113 |
| abstract_inverted_index.Franka | 177 |
| abstract_inverted_index.Sapien | 168 |
| abstract_inverted_index.expand | 137, 186 |
| abstract_inverted_index.expert | 41, 116 |
| abstract_inverted_index.failed | 125, 132 |
| abstract_inverted_index.method | 153 |
| abstract_inverted_index.online | 63 |
| abstract_inverted_index.policy | 151 |
| abstract_inverted_index.reward | 61, 91 |
| abstract_inverted_index.scores | 123 |
| abstract_inverted_index.solely | 38 |
| abstract_inverted_index.tasks. | 157, 203 |
| abstract_inverted_index.world. | 28 |
| abstract_inverted_index.Current | 29 |
| abstract_inverted_index.compute | 121 |
| abstract_inverted_index.conquer | 80 |
| abstract_inverted_index.crucial | 11 |
| abstract_inverted_index.dataset | 144, 189 |
| abstract_inverted_index.improve | 94, 195 |
| abstract_inverted_index.manner. | 104 |
| abstract_inverted_index.offline | 103 |
| abstract_inverted_index.process | 24 |
| abstract_inverted_index.quality | 122 |
| abstract_inverted_index.robotic | 13, 99, 155, 172, 201 |
| abstract_inverted_index.success | 197 |
| abstract_inverted_index.without | 87 |
| abstract_inverted_index.combines | 115 |
| abstract_inverted_index.dataset. | 140 |
| abstract_inverted_index.discards | 34 |
| abstract_inverted_index.enhanced | 143 |
| abstract_inverted_index.focusing | 37 |
| abstract_inverted_index.learning | 31, 45, 152 |
| abstract_inverted_index.reliance | 58 |
| abstract_inverted_index.segments | 129 |
| abstract_inverted_index.sim2real | 54 |
| abstract_inverted_index.training | 139, 188 |
| abstract_inverted_index.Extensive | 158 |
| abstract_inverted_index.Filtering | 111 |
| abstract_inverted_index.Improving | 0 |
| abstract_inverted_index.benchmark | 163 |
| abstract_inverted_index.challenge | 82 |
| abstract_inverted_index.difficult | 67 |
| abstract_inverted_index.expensive | 21 |
| abstract_inverted_index.failures, | 9, 52 |
| abstract_inverted_index.framework | 112 |
| abstract_inverted_index.imitation | 30 |
| abstract_inverted_index.imperfect | 5, 35, 85, 118, 192 |
| abstract_inverted_index.introduce | 107 |
| abstract_inverted_index.segments. | 127 |
| abstract_inverted_index.simulator | 169 |
| abstract_inverted_index.typically | 33 |
| abstract_inverted_index.ManiSkill2 | 162 |
| abstract_inverted_index.accurately | 185 |
| abstract_inverted_index.collection | 23 |
| abstract_inverted_index.downstream | 150 |
| abstract_inverted_index.especially | 3 |
| abstract_inverted_index.leveraging | 84 |
| abstract_inverted_index.real-world | 72, 171 |
| abstract_inverted_index.scenarios. | 73 |
| abstract_inverted_index.successful | 40 |
| abstract_inverted_index.trajectory | 126 |
| abstract_inverted_index.effectively | 70 |
| abstract_inverted_index.experiments | 159 |
| abstract_inverted_index.exploration | 64 |
| abstract_inverted_index.information | 92 |
| abstract_inverted_index.performance | 97 |
| abstract_inverted_index.High-quality | 128 |
| abstract_inverted_index.challenging, | 18 |
| abstract_inverted_index.demonstrated | 180 |
| abstract_inverted_index.explorations | 50 |
| abstract_inverted_index.high-quality | 191 |
| abstract_inverted_index.manipulation | 14, 100, 156, 173, 202 |
| abstract_inverted_index.utilization, | 2 |
| abstract_inverted_index.Specifically, | 105 |
| abstract_inverted_index.high-fidelity | 167 |
| abstract_inverted_index.reinforcement | 44 |
| abstract_inverted_index.Self-Supervised | 109 |
| abstract_inverted_index.time-consuming, | 19 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |