Symmetry-Aware Steering of Equivariant Diffusion Policies: Benefits and Limits
Symmetry-Aware Steering of Equivariant Diffusion Policies: Benefits and Limits
This research explores the application of symmetry-aware reinforcement learning to equivariant diffusion policies, showing that leveraging geometric symmetries improves sample efficiency, prevents value divergence, and enhances policy performance even with limited demonstration data.
This paper investigates the use of symmetry-aware reinforcement learning for fine-tuning equivariant diffusion policies, demonstrating improved sample efficiency and stability in policy improvement.
Source: arXiv Robotics — research abstracts · Read original article ↗
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ies across tasks with varying degrees of symmetry. While we identify the practical boundaries of strict equivariance under symmetry breaking, exploiting symmetry during the steering yields substantial benefits---enhancing sample efficiency, preventing value divergence, and achieving strong policy improvements even when EDPs are trained from extremely limited demonstrations.
Open source S4
es that EDPs are designed to exploit, leading to sample inefficiency and instability. We theoretically establish that the diffusion process of an EDP is equivariant, inducing a group-invariant latent-noise MDP which is well-suited for equivariant diffusion steering. Building on this, we introduce a principled symmetry-aware steering framework and compare standard, equivariant, and approximately equivariant RL strateg
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org