Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911180667224064 |
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| author | Bao, Lingfan Pan, Yan Peng, Tianhu Kanoulas, Dimitrios Zhou, Chengxu |
| author_facet | Bao, Lingfan Pan, Yan Peng, Tianhu Kanoulas, Dimitrios Zhou, Chengxu |
| contents | Effective human-robot interaction requires robots to identify human intentions and generate expressive, socially appropriate motions in real-time. Existing approaches often rely on fixed motion libraries or computationally expensive generative models. We propose a hierarchical framework that combines intention-aware reasoning via in-context learning (ICL) with real-time motion generation using diffusion models. Our system introduces structured prompting with confidence scoring, fallback behaviors, and social context awareness to enable intention refinement and adaptive response. Leveraging large-scale motion datasets and efficient latent-space denoising, the framework generates diverse, physically plausible gestures suitable for dynamic humanoid interactions. Experimental validation on a physical platform demonstrates the robustness and social alignment of our method in realistic scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots Bao, Lingfan Pan, Yan Peng, Tianhu Kanoulas, Dimitrios Zhou, Chengxu Robotics Effective human-robot interaction requires robots to identify human intentions and generate expressive, socially appropriate motions in real-time. Existing approaches often rely on fixed motion libraries or computationally expensive generative models. We propose a hierarchical framework that combines intention-aware reasoning via in-context learning (ICL) with real-time motion generation using diffusion models. Our system introduces structured prompting with confidence scoring, fallback behaviors, and social context awareness to enable intention refinement and adaptive response. Leveraging large-scale motion datasets and efficient latent-space denoising, the framework generates diverse, physically plausible gestures suitable for dynamic humanoid interactions. Experimental validation on a physical platform demonstrates the robustness and social alignment of our method in realistic scenarios. |
| title | Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.01563 |