Benchmarking Reinforcement Learning Techniques for Autonomous Navigation Article Swipe
Zifan Xu
,
Bo Liu
,
Xuesu Xiao
,
Anirudh Nair
,
Peter Stone
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.04839
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.04839
Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation. However, there still exists important limitations that prevent real-world use of RL-based navigation systems. For example, most learning approaches lack safety guarantees; and learned navigation systems may not generalize well to unseen environments. Despite a variety of recent learning techniques to tackle these challenges in general, a lack of an open-source benchmark and reproducible learning methods specifically for autonomous navigation makes it difficult for roboticists to choose what learning methods to use for their mobile robots and for learning researchers to identify current shortcomings of general learning methods for autonomous navigation. In this paper, we identify four major desiderata of applying deep RL approaches for autonomous navigation: (D1) reasoning under uncertainty, (D2) safety, (D3) learning from limited trial-and-error data, and (D4) generalization to diverse and novel environments. Then, we explore four major classes of learning techniques with the purpose of achieving one or more of the four desiderata: memory-based neural network architectures (D1), safe RL (D2), model-based RL (D2, D3), and domain randomization (D4). By deploying these learning techniques in a new open-source large-scale navigation benchmark and real-world environments, we perform a comprehensive study aimed at establishing to what extent can these techniques achieve these desiderata for RL-based navigation systems.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.04839
- https://arxiv.org/pdf/2210.04839
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4304732191
All OpenAlex metadata
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https://openalex.org/W4304732191Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2210.04839Digital Object Identifier
- Title
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Benchmarking Reinforcement Learning Techniques for Autonomous NavigationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-10Full publication date if available
- Authors
-
Zifan Xu, Bo Liu, Xuesu Xiao, Anirudh Nair, Peter StoneList of authors in order
- Landing page
-
https://arxiv.org/abs/2210.04839Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2210.04839Direct 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/2210.04839Direct OA link when available
- Concepts
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Reinforcement learning, Computer science, Benchmarking, Artificial intelligence, Benchmark (surveying), Machine learning, Generalization, Variety (cybernetics), Robot learning, Deep learning, Mobile robot, Robot, Mathematical analysis, Marketing, Geography, Geodesy, Mathematics, BusinessTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.general | 97 |
| abstract_inverted_index.learned | 35 |
| abstract_inverted_index.limited | 128 |
| abstract_inverted_index.methods | 67, 81, 99 |
| abstract_inverted_index.network | 162 |
| abstract_inverted_index.perform | 192 |
| abstract_inverted_index.prevent | 19 |
| abstract_inverted_index.purpose | 150 |
| abstract_inverted_index.safety, | 124 |
| abstract_inverted_index.systems | 37 |
| abstract_inverted_index.variety | 47 |
| abstract_inverted_index.However, | 12 |
| abstract_inverted_index.RL-based | 23, 209 |
| abstract_inverted_index.applying | 112 |
| abstract_inverted_index.example, | 27 |
| abstract_inverted_index.general, | 57 |
| abstract_inverted_index.identify | 93, 107 |
| abstract_inverted_index.learning | 2, 29, 50, 66, 80, 90, 98, 126, 146, 179 |
| abstract_inverted_index.systems. | 25, 211 |
| abstract_inverted_index.achieving | 152 |
| abstract_inverted_index.benchmark | 63, 187 |
| abstract_inverted_index.deploying | 177 |
| abstract_inverted_index.difficult | 74 |
| abstract_inverted_index.important | 16 |
| abstract_inverted_index.reasoning | 120 |
| abstract_inverted_index.successes | 7 |
| abstract_inverted_index.approaches | 30, 115 |
| abstract_inverted_index.autonomous | 9, 70, 101, 117 |
| abstract_inverted_index.challenges | 55 |
| abstract_inverted_index.desiderata | 110, 207 |
| abstract_inverted_index.generalize | 40 |
| abstract_inverted_index.navigation | 24, 36, 71, 186, 210 |
| abstract_inverted_index.real-world | 20, 189 |
| abstract_inverted_index.techniques | 51, 147, 180, 204 |
| abstract_inverted_index.desiderata: | 159 |
| abstract_inverted_index.guarantees; | 33 |
| abstract_inverted_index.large-scale | 185 |
| abstract_inverted_index.limitations | 17 |
| abstract_inverted_index.model-based | 168 |
| abstract_inverted_index.navigation. | 11, 102 |
| abstract_inverted_index.navigation: | 118 |
| abstract_inverted_index.open-source | 62, 184 |
| abstract_inverted_index.researchers | 91 |
| abstract_inverted_index.roboticists | 76 |
| abstract_inverted_index.establishing | 198 |
| abstract_inverted_index.memory-based | 160 |
| abstract_inverted_index.reproducible | 65 |
| abstract_inverted_index.shortcomings | 95 |
| abstract_inverted_index.specifically | 68 |
| abstract_inverted_index.uncertainty, | 122 |
| abstract_inverted_index.architectures | 163 |
| abstract_inverted_index.comprehensive | 194 |
| abstract_inverted_index.environments, | 190 |
| abstract_inverted_index.environments. | 44, 138 |
| abstract_inverted_index.randomization | 174 |
| abstract_inverted_index.reinforcement | 1 |
| abstract_inverted_index.generalization | 133 |
| abstract_inverted_index.trial-and-error | 129 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |