Koopman算子理论在灵巧操作技能学习中的效用研究
On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills
AI总结：
本研究探索Koopman算子理论在灵巧操作技能学习中的应用，提出基于该理论的模仿学习框架，可解析学习策略，显著降低计算负担并消除初始化敏感性，在成功率和样本效率上与最先进算法相当且速度更快。
摘要：
尽管基于学习的方法已经实现了令人瞩目的灵巧操作能力，但我们尚未看到其在资源充足的实验室之外得到广泛采用。这可能是由于实际限制，例如巨大的计算负担、难以理解的学习行为、对初始化的敏感性以及实施所需的大量专业技术。在这项工作中，我们研究了Koopman算子理论在缓解这些限制方面的效用。Koopman算子是简单而强大的控制理论结构，可以将复杂的非线性动力学表示为更高维度的线性系统。鉴于灵巧操作背后存在复杂的非线性动力学，我们开发了一个基于Koopman算子的模仿学习框架，以同时学习机械手和物体的期望运动。我们表明，Koopman算子对于灵巧操作出奇地有效，并提供了许多独特的优势。值得注意的是，策略可以解析地学习，大大减少了计算负担，消除了对初始化的敏感性以及对繁琐的超参数优化的需求。我们的实验表明，基于Koopman算子的方法在成功率和样本效率方面可以与最先进的模仿学习算法相媲美，同时速度提高一个数量级。策略视频可在https://sites.google.com/view/kodex-corl查看。
英文摘要：
Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practical limitations, such as significant computational burden, inscrutable learned behaviors, sensitivity to initialization, and the considerable technical expertise required for implementation. In this work, we investigate the utility of Koopman operator theory in alleviating these limitations. Koopman operators are simple yet powerful control-theoretic structures to represent complex nonlinear dynamics as linear systems in higher dimensions. Motivated by the fact that complex nonlinear dynamics underlie dexterous manipulation, we develop a Koopman operator-based imitation learning framework to learn the desired motions of both the robotic hand and the object simultaneously. We show that Koopman operators are surprisingly effective for dexterous manipulation and offer a number of unique benefits. Notably, policies can be learned analytically, drastically reducing computation burden and eliminating sensitivity to initialization and the need for painstaking hyperparameter optimization. Our experiments reveal that a Koopman operator-based approach can perform comparably to state-of-the-art imitation learning algorithms in terms of success rate and sample efficiency, while being an order of magnitude faster. Policy videos can be viewed at https://sites.google.com/view/kodex-corl.
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