We develop an approach for efficient open-vocabulary language-conditioned manipulation policy learning.
We study sample-efficient modeling of invariant drag coefficients with equivariant networks.
We study how to ground language for robotic grasping while preserve the geometric structure of its symmetry.
We study whether Euclidean symmetry can help in reinforcement learning and planning, which models the geometric transformations between reference frames of robots.
This work aims to improve the zero-shot generalization performance in map-based navigation on novel layouts.
This work proposes a reinforcement learning framework for compositional object-oriented environments.