# Benchmarking In-Hand Manipulation

arXiv: https://arxiv.org/abs/2001.03070 · HTML: https://mlsanity.com/paper/2001.03070/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation](https://mlsanity.com/paper/2305.12821/) (2023)
- [Research Challenges and Progress in Robotic Grasping and Manipulation Competitions](https://mlsanity.com/paper/2108.01483/) (2021)
- [Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions](https://mlsanity.com/paper/2206.14854/) (2022)
- [Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch](https://mlsanity.com/paper/2212.08604/) (2022)
- [Attention-based Estimation and Prediction of Human Intent to augment Haptic Glove aided Control of Robotic Hand](https://mlsanity.com/paper/2110.07953/) (2021)
- [DexHoldem: Playing Texas Hold'em with Dexterous Embodied System](https://mlsanity.com/paper/2605.18727/) (2026)

## Abstract (arXiv)

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/benchmarkinginhandmanipulation