Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation
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| Format: | Preprint |
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2026
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| _version_ | 1866912917861957632 |
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| author | Canh, Thanh Nguyen Tran, Thanh-Tuan Zhang, Haolan Gao, Ziyan Chong, Nak Young HoangVan, Xiem |
| author_facet | Canh, Thanh Nguyen Tran, Thanh-Tuan Zhang, Haolan Gao, Ziyan Chong, Nak Young HoangVan, Xiem |
| contents | Learning from Demonstration (LfD) offers a promising paradigm for robot skill acquisition. Recent approaches attempt to extract manipulation commands directly from video demonstrations, yet face two critical challenges: (1) general video captioning models prioritize global scene features over task-relevant objects, producing descriptions unsuitable for precise robotic execution, and (2) end-to-end architectures coupling visual understanding with policy learning require extensive paired datasets and struggle to generalize across objects and scenarios. To address these limitations, we propose a novel ``Human-to-Robot'' imitation learning pipeline that enables robots to acquire manipulation skills directly from unstructured video demonstrations, inspired by the human ability to learn by watching and imitating. Our key innovation is a modular framework that decouples the learning process into two distinct stages: (1) Video Understanding, which combines Temporal Shift Modules (TSM) with Vision-Language Models (VLMs) to extract actions and identify interacted objects, and (2) Robot Imitation, which employs TD3-based deep reinforcement learning to execute the demonstrated manipulations. We validated our approach in PyBullet simulation environments with a UR5e manipulator and in a real-world experiment with a UF850 manipulator across four fundamental actions: reach, pick, move, and put. For video understanding, our method achieves 89.97% action classification accuracy and BLEU-4 scores of 0.351 on standard objects and 0.265 on novel objects, representing improvements of 76.4% and 128.4% over the best baseline, respectively. For robot manipulation, our framework achieves an average success rate of 87.5% across all actions, with 100% success on reaching tasks and up to 90% on complex pick-and-place operations. The project website is available at https://thanhnguyencanh.github.io/LfD4hri. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_19184 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation Canh, Thanh Nguyen Tran, Thanh-Tuan Zhang, Haolan Gao, Ziyan Chong, Nak Young HoangVan, Xiem Robotics Learning from Demonstration (LfD) offers a promising paradigm for robot skill acquisition. Recent approaches attempt to extract manipulation commands directly from video demonstrations, yet face two critical challenges: (1) general video captioning models prioritize global scene features over task-relevant objects, producing descriptions unsuitable for precise robotic execution, and (2) end-to-end architectures coupling visual understanding with policy learning require extensive paired datasets and struggle to generalize across objects and scenarios. To address these limitations, we propose a novel ``Human-to-Robot'' imitation learning pipeline that enables robots to acquire manipulation skills directly from unstructured video demonstrations, inspired by the human ability to learn by watching and imitating. Our key innovation is a modular framework that decouples the learning process into two distinct stages: (1) Video Understanding, which combines Temporal Shift Modules (TSM) with Vision-Language Models (VLMs) to extract actions and identify interacted objects, and (2) Robot Imitation, which employs TD3-based deep reinforcement learning to execute the demonstrated manipulations. We validated our approach in PyBullet simulation environments with a UR5e manipulator and in a real-world experiment with a UF850 manipulator across four fundamental actions: reach, pick, move, and put. For video understanding, our method achieves 89.97% action classification accuracy and BLEU-4 scores of 0.351 on standard objects and 0.265 on novel objects, representing improvements of 76.4% and 128.4% over the best baseline, respectively. For robot manipulation, our framework achieves an average success rate of 87.5% across all actions, with 100% success on reaching tasks and up to 90% on complex pick-and-place operations. The project website is available at https://thanhnguyencanh.github.io/LfD4hri. |
| title | Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.19184 |