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HyReach: Vision-Guided Hybrid Manipulator Reaching in Cluttered Unseen Environments.

Shivani Kamtikar^1,2, Kendall Koe¹, Justin Wasserman³

¹University of Illinois Urbana-Champaign, Urbana, Illinois, USA.

|April 27, 2026

Summary

This study introduces a hybrid rigid-soft robotic arm for navigating cluttered environments. The system achieves precise object reaching in unseen settings without retraining, demonstrating robust adaptability.

Keywords:

cluttered environments hybrid manipulators learning-based methods obstacle avoidance soft continuum arms

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Area of Science:

Robotics
Artificial Intelligence
Mechanical Engineering

Background:

Robotic systems require advanced manipulators for unstructured environments.
Existing manipulators often lack the necessary compliance, adaptability, and precision.

Purpose of the Study:

To present a real-time hybrid rigid-soft continuum manipulator.
To enable robust open-world object reaching in challenging environments.

Main Methods:

Integrating vision-based perception and 3D scene reconstruction.
Utilizing shape-aware motion planning for safe trajectory generation.
Employing a learning-based controller for hybrid arm control.

Main Results:

Consistent reaching performance with errors below 2 cm.
Successful operation across diverse cluttered real-world setups.
Demonstrated generalization to new, unseen scenes without retraining.

Conclusions:

Hybrid manipulators offer adaptive and reliable operation in unstructured environments.
The developed system shows significant potential for real-world robotic applications.