MULTI PATH GAIT CONTROL METHOD FOR BIPEDAL ROBOTS BASED ON DEEP REINFORCEMENT LEARNING.
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| Title: | MULTI PATH GAIT CONTROL METHOD FOR BIPEDAL ROBOTS BASED ON DEEP REINFORCEMENT LEARNING. |
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| Authors: | LEHUI LIN1 linlehui@dufe.edu.cn, PINGLI LV2 |
| Source: | Scalable Computing: Practice & Experience. Sep2025, Vol. 26 Issue 5, p1964-1973. 10p. |
| Subjects: | Deep reinforcement learning, Machine learning, Robot motion, PID controllers, Motion control devices |
| Abstract: | We propose a multipath gait control strategy based on deep reinforcement learning (DRL) for bipedal robot motion planning on diverse and challenging terrains. Traditional control methods, such as PID controllers and model-based motion planning, often struggle in complex environments. These approaches typically underperform because they rely on precise mathematical models or predefined rules, making them ill-suited for nonlinear, uncertain, and dynamic settings. Conventional techniques also have difficulty adapting their control strategies in unpredictable and fluctuating terrains, where robots may encounter unforeseen disturbances, leading to instability or failure. Deep reinforcement learning is able to independently acquire optimal control methods from environmental feedback without requiring a precise model since it combines deep learning and reinforcement learning. In this work, we leverage deep reinforcement learning algorithms (DDPG, TRPO, PPO, A3C, SAC, etc.) based on actor-critic (AC) architectures to enable reliable gait control of bipedal robots in a continuous motion environment. The issue that traditional approaches have in challenging to converge complicated environments is solved by DRL, which, when compared to traditional methods, can effectively cope with the high nonlinearity of complex terrain and adaptively alter the strategy through continuous contact with the environment. Using goal-conditional techniques, we created a motion planning model and tested it on the actual hardware platform Cassie. According to the experimental results, the approach successfully transfers the simulation strategy to the actual environment, and the robot can accurately complete the goal task without global location feedback. It can also perform a variety of complex tasks, like jumping on discontinuous and flat terrain. Furthermore, the method exhibits significant robustness and adaptability through multithreaded asynchronous training and randomized strategy selection, which solves the shortcomings of conventional motion planning methods in hyperparameter tuning and strategy convergence. [ABSTRACT FROM AUTHOR] |
| Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 186692647 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: MULTI PATH GAIT CONTROL METHOD FOR BIPEDAL ROBOTS BASED ON DEEP REINFORCEMENT LEARNING. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22LEHUI+LIN%22">LEHUI LIN</searchLink><relatesTo>1</relatesTo><i> linlehui@dufe.edu.cn</i><br /><searchLink fieldCode="AR" term="%22PINGLI+LV%22">PINGLI LV</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Scalable+Computing%3A+Practice+%26+Experience%22">Scalable Computing: Practice & Experience</searchLink>. Sep2025, Vol. 26 Issue 5, p1964-1973. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+reinforcement+learning%22">Deep reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Robot+motion%22">Robot motion</searchLink><br /><searchLink fieldCode="DE" term="%22PID+controllers%22">PID controllers</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+control+devices%22">Motion control devices</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We propose a multipath gait control strategy based on deep reinforcement learning (DRL) for bipedal robot motion planning on diverse and challenging terrains. Traditional control methods, such as PID controllers and model-based motion planning, often struggle in complex environments. These approaches typically underperform because they rely on precise mathematical models or predefined rules, making them ill-suited for nonlinear, uncertain, and dynamic settings. Conventional techniques also have difficulty adapting their control strategies in unpredictable and fluctuating terrains, where robots may encounter unforeseen disturbances, leading to instability or failure. Deep reinforcement learning is able to independently acquire optimal control methods from environmental feedback without requiring a precise model since it combines deep learning and reinforcement learning. In this work, we leverage deep reinforcement learning algorithms (DDPG, TRPO, PPO, A3C, SAC, etc.) based on actor-critic (AC) architectures to enable reliable gait control of bipedal robots in a continuous motion environment. The issue that traditional approaches have in challenging to converge complicated environments is solved by DRL, which, when compared to traditional methods, can effectively cope with the high nonlinearity of complex terrain and adaptively alter the strategy through continuous contact with the environment. Using goal-conditional techniques, we created a motion planning model and tested it on the actual hardware platform Cassie. According to the experimental results, the approach successfully transfers the simulation strategy to the actual environment, and the robot can accurately complete the goal task without global location feedback. It can also perform a variety of complex tasks, like jumping on discontinuous and flat terrain. Furthermore, the method exhibits significant robustness and adaptability through multithreaded asynchronous training and randomized strategy selection, which solves the shortcomings of conventional motion planning methods in hyperparameter tuning and strategy convergence. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.12694/scpe.v26i5.4790 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1964 Subjects: – SubjectFull: Deep reinforcement learning Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Robot motion Type: general – SubjectFull: PID controllers Type: general – SubjectFull: Motion control devices Type: general Titles: – TitleFull: MULTI PATH GAIT CONTROL METHOD FOR BIPEDAL ROBOTS BASED ON DEEP REINFORCEMENT LEARNING. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: LEHUI LIN – PersonEntity: Name: NameFull: PINGLI LV IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 18951767 Numbering: – Type: volume Value: 26 – Type: issue Value: 5 Titles: – TitleFull: Scalable Computing: Practice & Experience Type: main |
| ResultId | 1 |
