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Bioinspired multimodal robotics

  • Ziyu Ren
  • , Youning Duo
  • , Haoyuan Xu
  • , Yihui Zhang
  • , Xingjian Liu*
  • , Jamie Paik*
  • , Auke Ijspeert*
  • , Li Wen*
  • *此作品的通讯作者
  • Beihang University
  • Tsinghua University
  • Dalian University of Technology
  • Swiss Federal Institute of Technology Lausanne

科研成果: 期刊稿件 › 文献综述 › 同行评审

摘要

To survive in dynamic and unstructured environments, animals have developed extraordinary multimodal locomotion capabilities. This biological insight drives the innovation of bioinspired multimodal robots, which are de-fined as robotic systems integrating and transitioning between two or more distinct modes of bioinspired locomotion. This Review explores the historical progression, key design considerations, and current challenges faced in the realm of multimodal robotics. We highlight recent advancements in robotic body design, including the integration of soft materials, innovative structure repurposing strategies, and the deployment of multirobot systems. These advancements facilitate seamless mode transitions through both active and passive structural reconfigurations. In the realm of path planning and motion control, the paradigm is progressively shifting from traditional graph-based approaches and discrete controllers to learning-based frameworks. To address the current void of standardized benchmarks in this field, we propose five performance metrics—number of modes, marginal cost of modality, component repurpose percentage, transition cost, and performance improvement—that provide a quantitative framework for evaluating multimodal robots in terms of design effectiveness and operational performance. Last, we delineate a strategic roadmap for the future that advocates for the integration of physical and computational intelligence in multimodal robotics, arguing that this convergence is crucial for enabling real-time behavioral adaptations to complex environmental stimuli and thereby enhancing the robustness and functionality of multimodal robotic systems.

源语言英语
期刊论文编号eaea7639
期刊Science Robotics
卷11
期116
DOI
出版状态已出版 - 7月 2026

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