81 citations · 202 across the 38 of their papers we have counts for
11 papers · 1 filter
Latent Space Symmetry Discovery
Jianke Yang, Nima Dehmamy, Robin Walters +1
Equivariant neural networks require explicit knowledge of the symmetry group. Automatic symmetry discovery methods aim to relax this constraint and learn invariance and equivarianc…
Leveraging Symmetries in Pick and Place
Haojie Huang, Dian Wang, Arsh Tangri +2
Robotic pick and place tasks are symmetric under translations and rotations of both the object to be picked and the desired place pose. For example, if the pick object is rotated o…
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction
Ayan Chatterjee, Robin Walters, Giulia Menichetti +1
Link prediction is a crucial task in graph machine learning with diverse applications. We explore the interplay between node attributes and graph topology and demonstrate that inco…
Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?
Linfeng Zhao, Owen Howell, Jung Yeon Park +3
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…
Equivariant Single View Pose Prediction Via Induced and Restricted Representations
Owen Howell, David Klee, Ondrej Biza +2
Learning about the three-dimensional world from two-dimensional images is a fundamental problem in computer vision. An ideal neural network architecture for such tasks would levera…
On Robot Grasp Learning Using Equivariant Models
Xupeng Zhu, Dian Wang, Guanang Su +3
Real-world grasp detection is challenging due to the stochasticity in grasp dynamics and the noise in hardware. Ideally, the system would adapt to the real world by training direct…
One-shot Imitation Learning via Interaction Warping
Ondrej Biza, Skye Thompson, Kishore Reddy Pagidi +7
Imitation learning of robot policies from few demonstrations is crucial in open-ended applications. We propose a new method, Interaction Warping, for learning SE(3) robotic manipul…
Improving Convergence and Generalization Using Parameter Symmetries
Bo Zhao, Robert M. Gower, Robin Walters +1
In many neural networks, different values of the parameters may result in the same loss value. Parameter space symmetries are loss-invariant transformations that change the model p…
A General Theory of Correct, Incorrect, and Extrinsic Equivariance
Dian Wang, Xupeng Zhu, Jung Yeon Park +4
Although equivariant machine learning has proven effective at many tasks, success depends heavily on the assumption that the ground truth function is symmetric over the entire doma…
Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction
David M. Klee, Ondrej Biza, Robert Platt +1
Predicting the pose of objects from a single image is an important but difficult computer vision problem. Methods that predict a single point estimate do not predict the pose of ob…
Generative Adversarial Symmetry Discovery
Jianke Yang, Robin Walters, Nima Dehmamy +1
Despite the success of equivariant neural networks in scientific applications, they require knowing the symmetry group a priori. However, it may be difficult to know which symmetry…