Benchmarking Simulated Robotic Manipulation through a Real World Dataset
Not on the map. Why?
Abstract (arXiv)
We present a benchmark to facilitate simulated manipulation; an attempt to overcome the obstacles of physical benchmarks through the distribution of a real world, ground truth dataset. Users are given various simulated manipulation tasks with assigned protocols having the objective of replicating the real world results of a recorded dataset. The benchmark comprises of a range of metrics used to characterise the successes of submitted environments whilst providing insight into their deficiencies. We apply our benchmark to two simulation environments, PyBullet and V-Rep, and publish the results. All materials required to benchmark an environment, including protocols and the dataset, can be found at the benchmarks' website https://research.csiro.au/robotics/manipulation-benchmark/.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Nov 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 3; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2023Train Offline, Test Online: A Real Robot Learning Benchmarkcited by 47
- 2023Benchmarking the Sim-to-Real Gap in Cloth Manipulationcited by 17
- 2023Robotic Manipulation Datasets for Offline Compositional Reinforcement Learningcited by 6
- 2026GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Modelscited by 5
- 2024Closed Loop Interactive Embodied Reasoning for Robot Manipulationcited by 4
- 2026GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models6 Aug 2026
- 2024Closed Loop Interactive Embodied Reasoning for Robot Manipulation23 Apr 2024
- 2023Benchmarking the Sim-to-Real Gap in Cloth Manipulation14 Oct 2023
- 2023Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning13 Jul 2023
- 2023Train Offline, Test Online: A Real Robot Learning Benchmark1 Jun 2023