Integrating Symmetry into Differentiable Planning with Steerable Convolutions


We study how group symmetry helps improve data efficiency and generalization for end-to-end differentiable planning algorithms, specifically on 2D robotic path planning problems: navigation and manipulation. We first formalize the idea from Value Iteration Networks (VINs) on using convolutional networks for path planning, because it avoids explicitly constructing equivalence classes and enables end-to-end planning. We then show that value iteration can always be represented as some convolutional form for (2D) path planning, and name the resulting paradigm Symmetric Planner (SymPlan). In implementation, we use steerable convolution networks to incorporate symmetry. Our algorithms on navigation and manipulation, with given or learned maps, improve training efficiency and generalization performance by large margins over non-equivariant counterparts, VIN and GPPN.

In RLDM 2022, Under Conference Review
Linfeng Zhao
Linfeng Zhao
CS Ph.D. Student

I am a CS Ph.D. student at Khoury College of Computer Sciences of Northeastern University, advised by Prof. Lawson L.S. Wong. My research interests include reinforcement learning, artificial intelligence, and robotics.