Benchmarking In-Hand Manipulation

January 09, 2020 · Entered Twilight · 🏛 IEEE Robotics and Automation Letters

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Authors Silvia Cruciani, Balakumar Sundaralingam, Kaiyu Hang, Vikash Kumar, Tucker Hermans, Danica Kragic arXiv ID 2001.03070 Category cs.RO: Robotics Citations 44 Venue IEEE Robotics and Automation Letters Repository https://github.com/balakumar-s/bih_dataset Last Checked 22 days ago

Abstract

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