EDGI: Equivariant Diffusion for Planning with Embodied Agents

Embodied agents operate in a structured world, often solving tasks with spatial, temporal, and permutation symmetries. Most algorithms for planning and modelbased reinforcement learning (MBRL) do not take this rich geometric structure into account, leading to sample inefficiency and poor generalization.