A Rapid Deployment Pipeline for Autonomous Humanoid Grasping Based on Foundation Models
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917420151603200 |
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| author | Yan, Yifei Liao, Yankai Ye, Linqi |
| author_facet | Yan, Yifei Liao, Yankai Ye, Linqi |
| contents | Deploying a humanoid robot to manipulate a new object has traditionally required one to two days of effort: data collection, manual annotation, 3D model acquisition, and model training. This paper presents an end-to-end rapid deployment pipeline that integrates three foundation-model components to shorten the onboarding cycle for a new object to approximately 30 minutes: (i) Roboflow-based automatic annotation to assist in training a YOLOv8 object detector; (ii) 3D reconstruction based on Meta SAM 3D, which eliminates the need for a dedicated laser scanner; and (iii) zero-shot 6-DoF pose tracking based on FoundationPose, using the SAM~3D-generated mesh directly as the template. The estimated pose drives a Unity-based inverse kinematics planner, whose joint commands are streamed via UDP to a Unitree~G1 humanoid and executed through the Unitree SDK. We demonstrate detection accuracy of mAP@0.5 = 0.995, pose tracking precision of $σ< 1.05$ mm, and successful grasping on a real robot at five positions within the workspace. We further verify the generality of the pipeline on an automobile-window glue-application task. The results show that combining foundation models for perception with everyday imaging devices (e.g., smartphones) can substantially lower the deployment barrier for humanoid manipulation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17258 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Rapid Deployment Pipeline for Autonomous Humanoid Grasping Based on Foundation Models Yan, Yifei Liao, Yankai Ye, Linqi Robotics Deploying a humanoid robot to manipulate a new object has traditionally required one to two days of effort: data collection, manual annotation, 3D model acquisition, and model training. This paper presents an end-to-end rapid deployment pipeline that integrates three foundation-model components to shorten the onboarding cycle for a new object to approximately 30 minutes: (i) Roboflow-based automatic annotation to assist in training a YOLOv8 object detector; (ii) 3D reconstruction based on Meta SAM 3D, which eliminates the need for a dedicated laser scanner; and (iii) zero-shot 6-DoF pose tracking based on FoundationPose, using the SAM~3D-generated mesh directly as the template. The estimated pose drives a Unity-based inverse kinematics planner, whose joint commands are streamed via UDP to a Unitree~G1 humanoid and executed through the Unitree SDK. We demonstrate detection accuracy of mAP@0.5 = 0.995, pose tracking precision of $σ< 1.05$ mm, and successful grasping on a real robot at five positions within the workspace. We further verify the generality of the pipeline on an automobile-window glue-application task. The results show that combining foundation models for perception with everyday imaging devices (e.g., smartphones) can substantially lower the deployment barrier for humanoid manipulation tasks. |
| title | A Rapid Deployment Pipeline for Autonomous Humanoid Grasping Based on Foundation Models |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.17258 |