Bullet Arm: Robotic Manipulation Benchmark

This lecture presents BulletArm, an open-source robotic manipulation benchmark and learning framework developed by D. Wang, C. Kohler, X. Zhu, M. Jia, and R. Platt from Northeastern University. The lecture covers the design goals, reproducibility, extensibility, and performance of BulletArm. It also explores the benchmark tasks, action spaces, and baseline algorithms available in the framework. Additionally, it discusses reinforcement learning, imitation learning, few-shot learning, multi-task learning, dataset generation, and various applications supported by BulletArm.