标题： 小波策略：长任务策略学习的提升方案 显示英文标题

标题： Wavelet Policy: Lifting Scheme for Policy Learning in Long-Horizon Tasks

摘要： 策略学习专注于为具身人工智能系统中的智能体设计策略，以根据其感知状态执行最优动作。 策略学习中的一个关键挑战在于处理需要管理大量动作和观察序列的复杂、长期任务。 小波分析在信号处理中具有显著优势，特别是在多尺度分解信号以捕捉全局趋势和细粒度细节方面。 在本工作中，我们引入了一种新颖的小波策略学习框架，该框架利用小波变换来增强策略学习。 我们的方法利用可学习的多尺度小波分解，以促进对详细观察的分析以及在长时间序列上的稳健动作规划。 我们详细描述了我们的小波策略的设计和实现，该策略结合了提升方案以进行有效的多分辨率分析和动作生成。 该框架在多个复杂场景中进行了评估，包括机器人操作、自动驾驶和多机器人协作，展示了我们的方法在提高学习策略的精度和可靠性方面的有效性。 显示英文摘要

摘要： Policy learning focuses on devising strategies for agents in embodied artificial intelligence systems to perform optimal actions based on their perceived states. One of the key challenges in policy learning involves handling complex, long-horizon tasks that require managing extensive sequences of actions and observations with multiple modes. Wavelet analysis offers significant advantages in signal processing, notably in decomposing signals at multiple scales to capture both global trends and fine-grained details. In this work, we introduce a novel wavelet policy learning framework that utilizes wavelet transformations to enhance policy learning. Our approach leverages learnable multi-scale wavelet decomposition to facilitate detailed observation analysis and robust action planning over extended sequences. We detail the design and implementation of our wavelet policy, which incorporates lifting schemes for effective multi-resolution analysis and action generation. This framework is evaluated across multiple complex scenarios, including robotic manipulation, self-driving, and multi-robot collaboration, demonstrating the effectiveness of our method in improving the precision and reliability of the learned policy.