# BulletArm: An Open-Source Robotic Manipulation Benchmark and Learning Framework

arXiv: https://arxiv.org/abs/2205.14292 · HTML: https://mlsanity.com/paper/2205.14292/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2022.

## Built on by (most cited first, 9 of 9)

- [The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry](https://mlsanity.com/paper/2211.09231/) (2022)
- [SEIL: Simulation-augmented Equivariant Imitation Learning](https://mlsanity.com/paper/2211.00194/) (2022)
- [A General Theory of Correct, Incorrect, and Extrinsic Equivariance](https://mlsanity.com/paper/2303.04745/) (2023)
- [Leveraging Fully Observable Policies for Learning under Partial Observability](https://mlsanity.com/paper/2211.01991/) (2022)
- [Equivariant Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/2408.14336/) (2024)
- [NEBULA: Do We Evaluate Vision-Language-Action Agents Correctly?](https://mlsanity.com/paper/2510.16263/) (2025)
- [Learning from Pixels with Expert Observations](https://mlsanity.com/paper/2306.13872/) (2023)
- [Embodied4C: Measuring What Matters for Embodied Vision-Language Navigation](https://mlsanity.com/paper/2512.18028/) (2025)
- [Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulation](https://mlsanity.com/paper/2508.11204/) (2025)

## Abstract (arXiv)

We present BulletArm, a novel benchmark and learning-environment for robotic manipulation. BulletArm is designed around two key principles: reproducibility and extensibility. We aim to encourage more direct comparisons between robotic learning methods by providing a set of standardized benchmark tasks in simulation alongside a collection of baseline algorithms. The framework consists of 31 different manipulation tasks of varying difficulty, ranging from simple reaching and picking tasks to more realistic tasks such as bin packing and pallet stacking. In addition to the provided tasks, BulletArm has been built to facilitate easy expansion and provides a suite of tools to assist users when adding new tasks to the framework. Moreover, we introduce a set of five benchmarks and evaluate them using a series of state-of-the-art baseline algorithms. By including these algorithms as part of our framework, we hope to encourage users to benchmark their work on any new tasks against these baselines.