Sliding Touch-Based Exploration for Modeling Unknown Object Shape with Multi-Fingered Hands
Paper in proceeding, 2023

Efficient and accurate 3D object shape reconstruction contributes significantly to the success of a robot's physical interaction with its environment. Acquiring accurate shape information about unknown objects is challenging, especially in unstructured environments, e.g. the vision sensors may only be able to provide a partial view. To address this issue, tactile sensors could be employed to extract local surface information for more robust unknown object shape estimation. In this paper, we propose a novel approach for efficient unknown 3D object shape exploration and reconstruction using a multi-fingered hand equipped with tactile sensors and a depth camera only providing a partial view. We present a multi-finger sliding touch strategy for efficient shape exploration using a Bayesian Optimization approach and a single-leader-multi-follower strategy for multi-finger smooth local surface perception. We evaluate our proposed method by estimating the 3D shape of objects from the YCB and OCRTOC datasets based on simulation and real robot experiments. The proposed approach yields successful reconstruction results relying on only a few continuous sliding touches. Experimental results demonstrate that our method is able to model unknown objects in an efficient and accurate way.

Bayesian Optimization

Tactile servoing

computer vision

Gaussian Processes

tactile sensing

robotics

Author

Yiting Chen

Chalmers University of Technology

Ahmet Ercan Tekden

Chalmers, Electrical Engineering, Systems and control

Marc Peter Deisenroth

University College London (UCL)

Yasemin Bekiroglu

Chalmers, Electrical Engineering, Systems and control

University College London (UCL)

IEEE International Conference on Intelligent Robots and Systems

21530858 (ISSN) 21530866 (eISSN)

8943-8950
9781665491907 (ISBN)

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Subject Categories

Robotics

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/IROS55552.2023.10342303

More information

Latest update

5/21/2024