基于局部轨迹调制的通用技能交互式增量学习
Interactive incremental learning of generalizable skills with local trajectory modulation
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中文总结 AI 辅助
提出基于KMP的交互式模仿学习框架，结合局部和全局轨迹调制，利用人类纠正反馈和经由点机制增量提升模型精度、添加新物体并扩展技能区域。
中文摘要 AI 辅助
多年来，从演示中学习（LfD）的泛化问题备受关注，特别是在运动基元背景下，出现了许多方法。最近，两种重要方法获得了认可。一种利用经由点通过调制演示轨迹来局部适应技能，另一种依赖所谓的任务参数化模型，利用概率乘积进行泛化，该模型将运动编码至不同坐标系。前者适合精确的局部调制，后者旨在对工作空间的大范围区域进行泛化，且通常涉及多个物体。通过同时利用这两种方法来解决泛化质量问题鲜少受到关注。在本研究中，我们提出了一个交互式模仿学习框架，同时利用轨迹分布的局部和全局调制。基于核化运动基元（KMP）框架，我们引入了基于人类直接纠正反馈的技能调制新机制。我们的方法特别利用经由点概念，以增量和交互的方式：1）局部提升模型精度；2）在执行期间向任务添加新物体；3）将技能扩展到未提供演示的区域。我们使用扭矩控制的7自由度DLR SARA机器人在轴承环装配任务上评估了该方法。
英文摘要
The problem of generalization in learning from demonstration (LfD) has received considerable attention over the years, particularly within the context of movement primitives, where a number of approaches have emerged. Recently, two important approaches have gained recognition. While one leverages via-points to adapt skills locally by modulating demonstrated trajectories, another relies on so-called task-parameterized models that encode movements with respect to different coordinate systems, using a product of probabilities for generalization. While the former are well-suited to precise, local modulations, the latter aim at generalizing over large regions of the workspace and often involve multiple objects. Addressing the quality of generalization by leveraging both approaches simultaneously has received little attention. In this work, we propose an interactive imitation learning framework that simultaneously leverages local and global modulations of trajectory distributions. Building on the kernelized movement primitives (KMP) framework, we introduce novel mechanisms for skill modulation from direct human corrective feedback. Our approach particularly exploits the concept of via-points to incrementally and interactively 1) improve the model accuracy locally, 2) add new objects to the task during execution and 3) extend the skill into regions where demonstrations were not provided. We evaluate our method on a bearing ring-loading task using a torque-controlled, 7-DoF, DLR SARA robot.
发表机构
- German Aerospace Center (DLR)（德国航空航天中心）
- School of Computation, Information and Technology (CIT), Technical University of Munich (TUM)（慕尼黑工业大学计算、信息与技术学院）
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