CCDP：带引导采样的条件扩散策略组合
CCDP: Composition of Conditional Diffusion Policies with Guided Sampling
AI总结：
本文提出CCDP方法，通过细化采样分布避免重复失败动作，并利用扩散模型分解将长时程问题分解为子问题，在门开启、物体操作等任务中优于传统基线。
摘要：
模仿学习提供了一种有前景的方法，可以直接从数据中学习，而无需显式模型、模拟或详细的任务定义。在推理过程中，从学习到的分布中采样动作并在机器人上执行。然而，采样的动作可能因各种原因失败，简单地重复采样步骤直到获得成功动作可能效率低下。在这项工作中，我们提出了一种增强的采样策略，该策略细化采样分布以避免先前不成功的动作。我们证明，仅利用成功演示的数据，我们的方法就能推断出恢复动作，而无需额外的探索行为或高级控制器。此外，我们利用扩散模型分解的概念，将主要问题（可能需要在长时程历史中管理失败）分解为多个更小、更易管理的子问题，涉及学习、数据收集和推理，从而使系统能够适应可变的失败次数。我们的方法产生了一个低级控制器，当先前的样本不足时，它会动态调整其采样空间以提高效率。我们在多个任务上验证了我们的方法，包括未知方向的门开启、物体操作和按钮搜索场景，证明我们的方法优于传统基线。
英文摘要：
Imitation Learning offers a promising approach to learn directly from data without requiring explicit models, simulations, or detailed task definitions. During inference, actions are sampled from the learned distribution and executed on the robot. However, sampled actions may fail for various reasons, and simply repeating the sampling step until a successful action is obtained can be inefficient. In this work, we propose an enhanced sampling strategy that refines the sampling distribution to avoid previously unsuccessful actions. We demonstrate that by solely utilizing data from successful demonstrations, our method can infer recovery actions without the need for additional exploratory behavior or a high-level controller. Furthermore, we leverage the concept of diffusion model decomposition to break down the primary problem, which may require long-horizon history to manage failures, into multiple smaller, more manageable sub-problems in learning, data collection, and inference, thereby enabling the system to adapt to variable failure counts. Our approach yields a low-level controller that dynamically adjusts its sampling space to improve efficiency when prior samples fall short. We validate our method across several tasks, including door opening with unknown directions, object manipulation, and button-searching scenarios, demonstrating that our approach outperforms traditional baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
