Data-efficient Learning of Morphology and Controller for a Microrobot
Data-efficient Learning of Morphology and Controller for a Microrobot
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微型机器人形态学和控制器的数据高效学习
DOI：
10.1109/icra.2019.8793802
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
R. Calandra
中科院分区：
文献类型：
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作者：
Thomas Liao;Grant Wang;Brian Yang;R. Lee;K. Pister;S. Levine;R. Calandra
机器人设计通常是一个缓慢而困难的过程，需要迭代地构建和测试原型，目标是顺序地优化设计。对于大多数机器人来说，当验证硬件解决所需任务的能力时，需要已经拥有适当的控制器，而控制器又是针对特定硬件进行设计和调整的，这使得这一过程变得更加复杂。在本文中，我们提出了一种新的方法，HPC-BBO，用于高效和自动地设计硬件配置，并通过自动调整相应的控制器来评估它们。HPC-BBO基于分层贝叶斯优化过程，该过程迭代地优化形态配置(基于控制器学习过程中先前设计的性能)，并随后学习相应的控制器(利用从先前形态优化收集的知识)。此外，HPC-BBO可以一次选择一批多个形态设计，从而实现硬件验证的并行化，减少耗时的生产周期。以模拟六足微机器人为例，验证了HPC-BBO的形态和控制器设计的有效性。实验结果表明，HPC-BBO的性能优于多个竞争基线，生产周期比标准贝叶斯优化减少360%，从而将我们假设的微机器人制造时间从21个月缩短到4个月。
Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes, with the goal of sequentially optimizing the design. For most robots, this process is further complicated by the need, when validating the capabilities of the hardware to solve the desired task, to already have an appropriate controller, which is in turn designed and tuned for the specific hardware. In this paper, we propose a novel approach, HPC-BBO, to efficiently and automatically design hardware configurations, and evaluate them by also automatically tuning the corresponding controller. HPC-BBO is based on a hierarchical Bayesian optimization process which iteratively optimizes morphology configurations (based on the performance of the previous designs during the controller learning process) and subsequently learns the corresponding controllers (exploiting the knowledge collected from optimizing for previous morphologies). Moreover, HPC-BBO can select a “batch” of multiple morphology designs at once, thus parallelizing hardware validation and reducing the number of time-consuming production cycles. We validate HPC-BBO on the design of the morphology and controller for a simulated 6-legged microrobot. Experimental results show that HPC-BBO outperforms multiple competitive baselines, and yields a 360% reduction in production cycles over standard Bayesian optimization, thus reducing the hypothetical manufacturing time of our microrobot from 21 to 4 months.