Recent works have shown the promise of inference-time search over action samples for improving generative robot policies. In particular, optimizing cross-chunk coherence via bidirectional decoding has proven effective in boosting the consistency and reactivity of diffusion policies. However, this approach remains computationally expensive as the diversity of sampled actions grows. In this paper, we introduce self-guided action diffusion, a more efficient variant of bidirectional decoding tailored for diffusion-based policies. At the core of our method is to guide the proposal distribution at each diffusion step based on the prior decision. Experiments in simulation tasks show that the proposed self-guidance enables near-optimal performance at negligible inference cost. Notably, under a tight sampling budget, our method achieves up to 70% higher success rates than existing counterparts on challenging dynamic tasks. See project website at https://rhea-mal.github.io/selfgad.github.io.

翻译：近期研究表明，在推理阶段对动作样本进行搜索能够有效提升生成式机器人策略的性能。特别是通过双向解码优化跨片段一致性的方法，已被证明能显著增强扩散策略的一致性与反应能力。然而，随着采样动作多样性的增加，该方法的计算开销依然较大。本文提出自引导动作扩散，这是一种针对基于扩散的策略而设计的、更高效的双向解码变体。我们方法的核心在于依据先前的决策，在扩散过程的每一步中引导提议分布。仿真实验表明，所提出的自引导机制能够以可忽略的推理成本实现接近最优的性能。值得注意的是，在严格的采样预算下，本方法在具有挑战性的动态任务上比现有方法取得了高达70%的成功率提升。项目网站详见 https://rhea-mal.github.io/selfgad.github.io。