The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems. The goal is to assess the system's ability to change the pose of a hand-held object by either using the fingers, environment or a combination of both. Given an object surface mesh from the YCB data-set, we provide examples of initial and goal states (i.e.\ static object poses and fingertip locations) for various in-hand manipulation tasks. We further propose metrics that measure the error in reaching the goal state from a specific initial state, which, when aggregated across all tasks, also serves as a measure of the system's in-hand manipulation capability. We provide supporting software, task examples, and evaluation results associated with the benchmark. All the supporting material is available at https://robot-learning.cs.utah.edu/project/benchmarking_in_hand_manipulation
@article{arxiv.2001.03070,
title = {Benchmarking In-Hand Manipulation},
author = {Silvia Cruciani and Balakumar Sundaralingam and Kaiyu Hang and Vikash Kumar and Tucker Hermans and Danica Kragic},
journal= {arXiv preprint arXiv:2001.03070},
year = {2020}
}
Comments
Accepted to Robotics Automation and Letters (RA-L)