Human-in-the-loop Pose Estimation via Shared Autonomy

Abstract

Reliable, efficient shared autonomy requires balancing human operation and robot automation on complex tasks, such as dexterous manipulation. Adding to the difficulty of shared autonomy is a robot’s limited ability to perceive the 6 degree-of-freedom pose of objects, which is essential to perform manipulations those objects afforded. Inspired by Monte Carlo Localization, we propose a generative human-in-the-loop approach to estimating object pose. We characterize the performance of our mixed-initiative 3D registration approach using 2D pointing devices via a user study. Seeking an analog for Fitts’s Law for 3D registration, we introduce a new evaluation framework that takes the entire registration process into account instead of only the outcome. When combined with estimates of registration confidence, we posit that mixed-initiative registration will reduce the human workload while maintaining or even improving final pose estimation accuracy.