Symmetry

Open-vocabulary Pick and Place via Patch-level Semantic Maps

We develop an approach for efficient open-vocabulary language-conditioned manipulation policy learning.

Equivariant Action Sampling for Reinforcement Learning and Planning

We study the problem of action sampling and propose a method to incorporate equivariance properties to the action sampling procedure.

Sample Efficient Modeling of Drag Coefficients for Satellites with Symmetry

We study sample-efficient modeling of invariant drag coefficients with equivariant networks.

Language Conditioned Equivariant Grasp

We study how to ground language for robotic grasping while preserve the geometric structure of its symmetry.

E(2)-Equivariant Graph Planning for Navigation

We study E(2) Euclidean equivariance in navigation on geometric graphs and develop message passing network to solve it.

Can Euclidean Symmetry Help in Reinforcement Learning and Planning?

We study whether Euclidean symmetry can help in reinforcement learning and planning, which models the geometric transformations between reference frames of robots.

Equivariant Single View Pose Prediction Via Induced and Restriction Representations

We train an equivariant network for pose prediction from single 2D image by using induced and restricted representations.

Integrating Symmetry into Differentiable Planning with Steerable Convolutions

We formulate how differentiable planning algorithms can exploit inherent symmetry in path planning problems, named SymPlan, and propose practical algorithms.