Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer

TL;DR

This paper introduces an autonomous keypoint optimization method for robotic manipulators that enhances detection accuracy and transferability from simulation to real-world applications, improving tasks like pose estimation and tool tracking.

Contribution

It proposes a novel iterative algorithm to select optimal keypoints on robots for robust detection, addressing challenges with symmetric tools and sim-to-real transfer.

Findings

Optimized keypoints significantly improve detection performance.

Domain randomization bridges the gap between simulation and real-world.

Method enables accurate pose estimation and tool tracking in robotic applications.

Abstract

Keypoint detection is an essential building block for many robotic applications like motion capture and pose estimation. Historically, keypoints are detected using uniquely engineered markers such as checkerboards or fiducials. More recently, deep learning methods have been explored as they have the ability to detect user-defined keypoints in a marker-less manner. However, different manually selected keypoints can have uneven performance when it comes to detection and localization. An example of this can be found on symmetric robotic tools where DNN detectors cannot solve the correspondence problem correctly. In this work, we propose a new and autonomous way to define the keypoint locations that overcomes these challenges. The approach involves finding the optimal set of keypoints on robotic manipulators for robust visual detection and localization. Using a robotic simulator as a medium, our algorithm utilizes synthetic data for DNN training, and the proposed algorithm is used to optimize the selection of keypoints through an iterative approach. The results show that when using the optimized keypoints, the detection performance of the DNNs improved significantly. We further use the optimized keypoints for real robotic applications by using domain randomization to bridge the reality gap between the simulator and the physical world. The physical world experiments show how the proposed method can be applied to the wide-breadth of robotic applications that require visual feedback, such as camera-to-robot calibration, robotic tool tracking, and end-effector pose estimation.