Planning

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.

Practice Makes Perfect: Planning to Learn Skill Parameter Policies

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.

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.

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.

Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation

We study how implicit differentiation helps scale up and improve convergence of differentiable planning algorithms.