Riemann robot energy storage

Is Riemann a real-time equivariant robot manipulation imitation learning framework?

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.

How does Riemann model the action space of robot manipulation tasks?

RiEMann models the SE (3)-equivariant action space of robot manipulation tasks as target poses, consisting of a translational vector t ∈ R3 t ∈ R 3 and a rotation matrix R ∈ R9 R ∈ R 9, which are proven to be SE (3)-equivariant.

How does Riemann learn a manipulation task?

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.

Why is Riemann scalable?

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.

How fast can Riemann predict a type-L vector field?

Thanks to the end-to-end pipleine of RiEMann, the network forward speed can be 5.4 FPS, which leads to the near real-time following experiments as follows. Besides the target pose, RiEMann can also predict any type-l vector fields that are SE (3)-equivariant, as long as given demonstrations.

Can a high-power robot use a precharged or fueled energy storage device?

For a high-power robot, a precharged or fueled energy storage device is one of the most viable options. With continued advances in robotics, the demands for power systems have become more rigorous, particularly in pursuing higher power and energy density with safer operation and longer cycle life.