Auto-LfD：迈向闭环的示范学习
Auto-LfD: Towards Closing the Loop for Learning from Demonstrations
AI总结：
本文提出Auto-LfD框架，针对示范学习泛化评估缺失问题，以DMP和KMP为例自动优化超参数，无需人工干预，并通过真实机器人插销入孔和推动任务验证其适用性。
摘要：
在过去几年中，已有大量工作致力于提升机器人的泛化能力，其中示范学习（LfD）因其用户友好和数据高效的特点而备受关注。尽管已报道了许多LfD解决方案，但一个关键问题尚未得到妥善解决：我们如何评估LfD的泛化性能？例如，当机器人绘制一个需要经过新的期望点的字母时，它如何确保新轨迹与示范保持相似的形状？当新任务与示范区域相距甚远时，这个问题变得更加重要。为了解决这一问题，用户通常求助于手动调整LfD方法的超参数，直到获得满意的轨迹。在本文中，我们旨在为LfD提供闭环评估反馈，并以自动方式优化LfD。具体而言，我们以动态运动基元（DMP）和核化运动基元（KMP）为例，开发了一个通用优化框架，能够衡量DMP和KMP的泛化性能，并在无需任何人工输入的情况下自动优化其超参数。评估包括在真实机器人上进行的插销入孔任务和推动任务，证明了我们框架的适用性。
英文摘要：
Over the past few years, there have been numerous works towards advancing the generalization capability of robots, among which learning from demonstrations (LfD) has drawn much attention by virtue of its user-friendly and data-efficient nature. While many LfD solutions have been reported, a key question has not been properly addressed: how can we evaluate the generalization performance of LfD? For instance, when a robot draws a letter that needs to pass through new desired points, how does it ensure the new trajectory maintains a similar shape to the demonstration? This question becomes more relevant when a new task is significantly far from the demonstrated region. To tackle this issue, a user often resorts to manual tuning of the hyperparameters of an LfD approach until a satisfactory trajectory is attained. In this paper, we aim to provide closed-loop evaluative feedback for LfD and optimize LfD in an automatic fashion. Specifically, we consider dynamical movement primitives (DMP) and kernelized movement primitives (KMP) as examples and develop a generic optimization framework capable of measuring the generalization performance of DMP and KMP and auto-optimizing their hyperparameters without any human inputs. Evaluations including a peg-in-hole task and a pushing task on a real robot evidence the applicability of our framework.
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。
