HyReach: Vision-Guided Hybrid Manipulator Reaching in Unseen Cluttered Environments
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908994824568832 |
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| author | Kamtikar, Shivani Koe, Kendall Wasserman, Justin Marri, Samhita Walt, Benjamin Uppalapati, Naveen Kumar Krishnan, Girish Chowdhary, Girish |
| author_facet | Kamtikar, Shivani Koe, Kendall Wasserman, Justin Marri, Samhita Walt, Benjamin Uppalapati, Naveen Kumar Krishnan, Girish Chowdhary, Girish |
| contents | As robotic systems increasingly operate in unstructured, cluttered, and previously unseen environments, there is a growing need for manipulators that combine compliance, adaptability, and precise control. This work presents a real-time hybrid rigid-soft continuum manipulator system designed for robust open-world object reaching in such challenging environments. The system integrates vision-based perception and 3D scene reconstruction with shape-aware motion planning to generate safe trajectories. A learning-based controller drives the hybrid arm to arbitrary target poses, leveraging the flexibility of the soft segment while maintaining the precision of the rigid segment. The system operates without environment-specific retraining, enabling direct generalization to new scenes. Extensive real-world experiments demonstrate consistent reaching performance with errors below 2 cm across diverse cluttered setups, highlighting the potential of hybrid manipulators for adaptive and reliable operation in unstructured environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21421 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HyReach: Vision-Guided Hybrid Manipulator Reaching in Unseen Cluttered Environments Kamtikar, Shivani Koe, Kendall Wasserman, Justin Marri, Samhita Walt, Benjamin Uppalapati, Naveen Kumar Krishnan, Girish Chowdhary, Girish Robotics Artificial Intelligence As robotic systems increasingly operate in unstructured, cluttered, and previously unseen environments, there is a growing need for manipulators that combine compliance, adaptability, and precise control. This work presents a real-time hybrid rigid-soft continuum manipulator system designed for robust open-world object reaching in such challenging environments. The system integrates vision-based perception and 3D scene reconstruction with shape-aware motion planning to generate safe trajectories. A learning-based controller drives the hybrid arm to arbitrary target poses, leveraging the flexibility of the soft segment while maintaining the precision of the rigid segment. The system operates without environment-specific retraining, enabling direct generalization to new scenes. Extensive real-world experiments demonstrate consistent reaching performance with errors below 2 cm across diverse cluttered setups, highlighting the potential of hybrid manipulators for adaptive and reliable operation in unstructured environments. |
| title | HyReach: Vision-Guided Hybrid Manipulator Reaching in Unseen Cluttered Environments |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.21421 |