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Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System.

Luca Marchionna1, Giulio Pugliese1, Mauro Martini1,2

  • 1Department of Electronics and Telecommunications (DET), Politecnico di Torino, 10129 Torino, Italy.

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Summary

This study introduces a cost-effective robotic system for playing Jenga, utilizing a visual-based strategy and force sensing for precise block extraction. The robot successfully removed up to 14 blocks consecutively, demonstrating its manipulation capabilities.

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Jengadeep instance segmentationrobotic armsensor fusionvisual servoing

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • The Jenga game serves as a benchmark for developing advanced robotic manipulation strategies.
  • Complex tasks like industrial and surgical manipulation require multi-step planning, sensor fusion (visual and tactile), and precise robotic arm movements.

Purpose of the Study:

  • To propose a novel, affordable robotic architecture for playing Jenga.
  • To enable precise block extraction using a visual-based control strategy combined with force sensing.

Main Methods:

  • Utilized the e.Do 6DOF anthropomorphic manipulator, a depth camera, and a 1D force sensor.
  • Developed an instance segmentation deep learning model trained on synthetic data for block pose tracking.
  • Integrated visual servoing with a force threshold for safe block removal detection.

Main Results:

  • The system accurately aligned the end-effector with target blocks for push-based extraction.
  • The low-cost solution enabled precise block targeting and safe removal.
  • The robot achieved up to 14 consecutive successful block extractions.

Conclusions:

  • The proposed cost-effective robotic architecture effectively plays Jenga.
  • This approach demonstrates the potential for visual-based control and force sensing in complex manipulation tasks.
  • The system's success highlights advancements in affordable robotics for challenging applications.