BulletArm, v1.0.0, released June 24, 2022

BulletArm is a PyBullet based benchmark and learning-environment for robotic manipulation research. For additional information see the Github repository or the full documentaion.

This package is mainly comprised of two sub-packages: the BulletArm Simulator and the BulletArm Baselines. The simulator provides numerous tools to enable rapid development of robotic manipulation environments for use in robotic learning. It provides a number of environemnts, robots, planners, and tasks out-of-the-box. Additionally, BulletArm was designed with an emphasis on extensibility, allowing users to easily create new enviornments, tasks, and robots as needed. We aim to maintain and improve this package adding new features over time and hope to encourage the community to assist with this by adding any interesting tasks they might design.

Through the combination of these tasks with the provided baselines, we present a comprehensive benchmark for use in either open-loop or closed-loop control. This benchmark consists of 19 different manipulation tasks of varying difficuly, ranging from simple reaching and picking tasks to more realistic tasks such as bin packing and pallet stacking. These tasks can be seen below. We provide state-of-the-art baseline algorithms allowing users to compare their algorithms to our previous work on any of the existing tasks or novel tasks which they create.

For additional information please see the documentation.