通过物理感知神经架构高效知识转移以加速多旋翼控制策略学习
Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures
AI总结：
本研究提出了一种物理感知神经架构，通过库初始化方案实现多旋翼控制策略的高效知识转移，显著减少环境交互，提升控制性能。
摘要：
高效地训练机器人的控制策略是一个重大挑战，可以通过利用训练相似系统所获得的知识进行跨身体知识转移来显著受益。在本工作中，我们专注于通过库初始化方案加速策略训练，该方案能够在多旋翼配置之间实现有效的知识转移。通过利用结合强化学习控制器和监督控制分配网络的物理感知神经控制架构，我们实现了已训练策略的重用。为此，我们使用基于策略评估的相似性度量，从库中识别适合初始化的策略。我们证明了这种度量与达到目标性能所需减少的环境交互相关，并因此适用于初始化。广泛的模拟和实际实验确认了我们的控制架构实现了最先进的控制性能，并且我们的初始化方案在多样化的四旋翼和六旋翼设计中平均节省了73.5%的环境交互（与从头开始训练策略相比），为强化学习中的高效跨身体转移铺平了道路。
英文摘要：
Efficiently training control policies for robots is a major challenge that can greatly benefit from utilizing knowledge gained from training similar systems through cross-embodiment knowledge transfer. In this work, we focus on accelerating policy training using a library-based initialization scheme that enables effective knowledge transfer across multirotor configurations. By leveraging a physics-aware neural control architecture that combines a reinforcement learning-based controller and a supervised control allocation network, we enable the reuse of previously trained policies. To this end, we utilize a policy evaluation-based similarity measure that identifies suitable policies for initialization from a library. We demonstrate that this measure correlates with the reduction in environment interactions needed to reach target performance and is therefore suited for initialization. Extensive simulation and real-world experiments confirm that our control architecture achieves state-of-the-art control performance, and that our initialization scheme saves on average up to $73.5\%$ of environment interactions (compared to training a policy from scratch) across diverse quadrotor and hexarotor designs, paving the way for efficient cross-embodiment transfer in reinforcement learning.
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。
