We study the problem of action sampling and propose a method to incorporate equivariance properties to the action sampling procedure.
This work learns to navigate end-to-end with map input, aiming to generalize to novel map layouts in zero-shot.
We enable a robot to rapidly and autonomously specialize parameterized skills by planning to practice them. The robot decides what skills to practice and how to practice them. The robot is left alone for hours, repeatedly practicing and improving.
We study sample-efficient modeling of invariant drag coefficients with equivariant networks.
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.