Benchmarking In-Hand Manipulation

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

Accepted to Robotics Automation and Letters (RA-L)

References in corpus (5)

• Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols
• Learning Dexterous In-Hand Manipulation
• Reinforcement Learning for Pivoting Task
• In-Hand Manipulation via Motion Cones
• Pneumatic Shape-shifting Fingers to Reorient and Grasp

Cited by in corpus (4)

• MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets
• Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation
• Research Challenges and Progress in Robotic Grasping and Manipulation Competitions
• A Dataset and Benchmark for Robotic Cloth Unfolding Grasp Selection: The ICRA 2024 Cloth Competition