Visuo-Tactile Keypoint Correspondences for Object Manipulation

TL;DR

This paper introduces a visuo-tactile keypoint correspondence method that enhances robot object manipulation accuracy and efficiency without extensive training, validated through real-world experiments.

Contribution

It presents a novel visuo-tactile keypoint correspondence approach that improves manipulation precision and deployment efficiency in unstructured environments.

Findings

Achieved millimeter-level manipulation accuracy.

Reduced error margin compared to traditional vision methods.

Validated on real-world tasks like block alignment and gear insertion.

Abstract

This paper presents a novel manipulation strategy that uses keypoint correspondences extracted from visuo-tactile sensor images to facilitate precise object manipulation. Our approach uses the visuo-tactile feedback to guide the robot's actions for accurate object grasping and placement, eliminating the need for post-grasp adjustments and extensive training. This method provides an improvement in deployment efficiency, addressing the challenges of manipulation tasks in environments where object locations are not predefined. We validate the effectiveness of our strategy through experiments demonstrating the extraction of keypoint correspondences and their application to real-world tasks such as block alignment and gear insertion, which require millimeter-level precision. The results show an average error margin significantly lower than that of traditional vision-based methods, which is sufficient to achieve the target tasks.