Abstract
Learning to move is a primary goal for animals and robots, where ensuring safety is often important when optimizing control policies on the embodied systems. For complex tasks such as the control of human or humanoid control, the high-dimensional parameter space adds complexity to the safe optimization effort. Current safe exploration algorithms exhibit inefficiency and may even become infeasible with large high-dimensional input spaces. Furthermore, existing high-dimensional constrained optimization methods neglect safety in the search process. In this paper, we propose High-dimensional Safe Bayesian Optimization with local optimistic exploration (HdSafeBO), a novel approach designed to handle high-dimensional sampling problems under probabilistic safety constraints. We introduce a local optimistic strategy to efficiently and safely optimize the objective function, providing a probabilistic safety guarantee and a cumulative safety violation bound. Through the use of isometric embedding, HdSafeBO addresses problems ranging from a few hundred to several thousand dimensions while maintaining safety guarantees. To our knowledge, HdSafeBO is the first algorithm capable of optimizing the control of high-dimensional musculoskeletal systems with high safety probability. We also demonstrate the real-world applicability of HdSafeBO through its use in the safe online optimization of neural stimulation induced human motion control.
Abstract (translated)
学习运动是动物和机器人的一项主要目标,在优化控制策略时,确保系统的安全性往往至关重要。对于复杂的任务(如人类或类人机器人的控制),高维参数空间会增加安全优化的难度。目前的安全探索算法在面对大规模、高维度输入空间时效率低下,甚至可能变得不可行。此外,现有的高维约束优化方法通常忽视了搜索过程中的安全性问题。 本文提出了一种名为“High-dimensional Safe Bayesian Optimization with local optimistic exploration”(HdSafeBO)的新方法,专门用于处理具有概率安全约束的高维采样问题。我们引入了一个局部乐观策略来高效且安全地优化目标函数,并提供了概率性的安全保障和累积的安全性违规上限。通过等距嵌入技术,HdSafeBO能够解决从几百到几千维度的问题同时保持安全性保障。 据我们所知,HdSafeBO是首个能够在高维骨骼肌肉系统控制中实现高度安全性的算法。此外,我们还通过在神经刺激诱导的人体运动控制的安全在线优化中的应用,展示了HdSafeBO的实际世界适用性。
URL
https://arxiv.org/abs/2412.20350