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We present BulletArm, a novel benchmark and learning-environment for roboticmanipulation. BulletArm is designed around two key principles: reproducibilityand extensibility. We aim to encourage more direct comparisons between roboticlearning methods by providing a set of standardized benchmark tasks insimulation alongside a collection of baseline algorithms. The frameworkconsists of 31 different manipulation tasks of varying difficulty, ranging fromsimple reaching and picking tasks to more realistic tasks such as bin packingand pallet stacking. In addition to the provided tasks, BulletArm has beenbuilt to facilitate easy expansion and provides a suite of tools to assistusers when adding new tasks to the framework. Moreover, we introduce a set offive benchmarks and evaluate them using a series of state-of-the-art baselinealgorithms. By including these algorithms as part of our framework, we hope toencourage users to benchmark their work on any new tasks against thesebaselines.

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