We present RiEMann, an end-to-end near Real-time SE(3)-Equivariant Robot Manipulation imitation learning framework from scene point cloud input. Compared to previous methods that rely on descriptor field matching, RiEMann directly predicts the target poses of objects for manipulation without any object segmentation. RiEMann learns a manipulation task from scratch with 5 to 10 demonstrations, generalizes to unseen SE(3) transformations and instances of target objects, resists visual interference of distracting objects, and follows the near real-time pose change of the target object. The scalable action space of RiEMann facilitates the addition of custom equivariant actions such as the direction of turning the faucet, which makes articulated object manipulation possible for RiEMann. In simulation and real-world 6-DOF robot manipulation experiments, we test RiEMann on 5 categories of manipulation tasks with a total of 25 variants and show that RiEMann outperforms baselines in both task success rates and SE(3) geodesic distance errors on predicted poses (reduced by 68.6%), and achieves a 5.4 frames per second (FPS) network inference speed. Code and video results are available at https://riemann-web.github.io/.

RiEMann是一个端到端的近实时SE(3)-等变机器人操作仿真学习框架，从场景点云输入中直接预测目标物体的姿态，学习任务的过程中不需要目标物体分割，具有泛化到未见SE(3)变换和目标物体实例、抵抗干扰物体的视觉干扰以及跟踪目标物体的近实时姿态变化等特点。在仿真和真实世界的6自由度机器人操作实验中，通过对5类操作任务的25个变体进行测试，RiEMann在任务成功率和预测姿态的SE(3)测地距离误差方面均优于基准模型（误差减少了68.6%），并实现了每秒5.4帧的网络推断速度。

RiEMann：无需点云分割的近实时SE(3)-等变机器人操作