生成式机器人控制的性能关键不在多模态，而在迭代优化与适度随机性。
Much Ado About Noising: Dispelling the Myths of Generative Robotic Control
- 通过两步回归实现迭代优化，无需复杂分布建模
- 在行为克隆基准上达到流模型相当甚至更优表现
- 适合关注控制精度而非生成能力的研究者
生成模型如流模型和扩散模型近年在机器人控制中广泛应用，常被认为因其能捕捉多模态动作分布或表达复杂观测-动作映射而成功。本文在常见行为克隆（BC）基准上对主流生成控制策略（GCPs）进行全面评估，发现其优势并非源于多模态建模或复杂映射表达，而是得益于迭代计算：只要训练中监督中间步骤且搭配适当随机性，性能即显著提升。作为验证，我们提出的最小迭代策略（MIP）——一种基于两步回归的轻量级策略——在多数任务上表现与流模型相当，甚至优于压缩后的快捷模型。结果表明，生成模型中的分布拟合成分实际作用被高估，未来设计应更聚焦于控制性能本身。
原文摘要 · Abstract (English)
Generative models, like flows and diffusions, have recently emerged as popular and efficacious policy parameterizations in robotics. There has been much speculation as to the factors underlying their successes, ranging from capturing multi-modal action distribution to expressing more complex behaviors. In this work, we perform a comprehensive evaluation of popular generative control policies (GCPs) on common behavior cloning (BC) benchmarks. We find that GCPs do not owe their success to their ability to capture multi-modality or to express more complex observation-to-action mappings. Instead, we find that their advantage stems from iterative computation, as long as intermediate steps are supervised during training and this supervision is paired with a suitable level of stochasticity. As a validation of our findings, we show that a minimum iterative policy (MIP), a lightweight two-step regression-based policy, essentially matches the performance of flow GCPs, and often outperforms distilled shortcut models. Our results suggest that the distribution-fitting component of GCPs is less salient than commonly believed, and point toward new design spaces focusing solely on control performance. Project page: https://simchowitzlabpublic.github.io/much-ado-about-noising-project/
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