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 this https URL

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。