Optical Proximity Sensing for Pose Estimation During In-Hand Manipulation

TL;DR

This paper introduces a novel optical proximity sensor-based method for pose estimation during in-hand manipulation, demonstrating significant improvements over tactile sensors in accuracy and control performance.

Contribution

It presents the first evaluation of proximity sensors embedded in fingertips for pose estimation, showing their robustness and effectiveness in in-hand manipulation tasks.

Findings

Proximity sensors reduce pose estimation error by 50% compared to tactile sensors.

Using proximity sensors improves final positioning accuracy by 30%.

Proximity-based pose estimation enables more precise control during manipulation.

Abstract

During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates.