2020/01/07 by Silvia Cruciani, Balakumar Sundaralingam, Kaiyu Hang +4 · 49 citations
Computer Science · Engineering · #Artificial intelligence #Benchmark (surveying) #Benchmarking #Computer science #Data mining #Engineering #Human–computer interaction #Machine learning #Measure (data warehouse) #Object (grammar) #Programming language #Robot #Robot Manipulation and Learning #Robotic Path Planning Algorithms #Robotics #Set (abstract data type) #Software #Systems engineering #Task (project management) #Teleoperation and Haptic Systems #cs.RO
paper · pdf · doi:10.1109/lra.2020.2964160
published in IEEE Robotics and Automation Letters 5(2), 588-595 (Institute of Electrical and Electronics Engineers) · Accepted to Robotics Automation and Letters (RA-L)
openalex publication_date 2020/01/07 · arxiv created 2020/01/09 · arxiv updated 2020/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.