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Found 6 papers, 1 papers with code
Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
Many complex robotic manipulation tasks can be decomposed as a sequence of pick and place actions.
Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?
In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws. These changes, which preserve distance, encompass isometric transformations such as translations, rotations, and reflections, collectively known as the Euclidean group.
On Robot Grasp Learning Using Equivariant Models
Real-world grasp detection is challenging due to the stochasticity in grasp dynamics and the noise in hardware.
Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection
Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped.
Integrating Symmetry into Differentiable Planning with Steerable Convolutions
Our implementation is based on VINs and uses steerable convolution networks to incorporate symmetry.
Equivariant Grasp learning In Real Time
Visual grasp detection is a key problem in robotics where the agent must learn to model the grasp function, a mapping from an image of a scene onto a set of feasible grasp poses.
