# GRASPA 1.0: GRASPA is a Robot Arm graSping Performance benchmArk

arXiv: https://arxiv.org/abs/2002.05017 · HTML: https://mlsanity.com/paper/2002.05017/

## Key facts

- Cited by 5 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [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)
- [SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes](https://mlsanity.com/paper/2306.15620/) (2023)
- [Distracted Robot: How Visual Clutter Undermine Robotic Manipulation](https://mlsanity.com/paper/2511.22780/) (2025)
- [SCOPE: Semantic Conditioning for Sim2Real Category-Level Object Pose Estimation in Robotics](https://mlsanity.com/paper/2509.24572/) (2025)

## Abstract (arXiv)

The use of benchmarks is a widespread and scientifically meaningful practice to validate performance of different approaches to the same task. In the context of robot grasping the use of common object sets has emerged in recent years, however no dominant protocols and metrics to test grasping pipelines have taken root yet. In this paper, we present version 1.0 of GRASPA, a benchmark to test effectiveness of grasping pipelines on physical robot setups. This approach tackles the complexity of such pipelines by proposing different metrics that account for the features and limits of the test platform. As an example application, we deploy GRASPA on the iCub humanoid robot and use it to benchmark our grasping pipeline. As closing remarks, we discuss how the GRASPA indicators we obtained as outcome can provide insight into how different steps of the pipeline affect the overall grasping performance.