Learning from Demonstration (LfD) aims to encode versatile skills from human demonstrations. The field has been gaining popularity since it facilitates knowledge transfer to robots without requiring expert knowledge in robotics. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this, task-parameterized LfD (TP-LfD) encodes relevant contextual information in reference frames, enabling better skill generalization to new situations. However, most TP-LfD algorithms require multiple demonstrations in various environment conditions to ensure sufficient statistics for a meaningful model. It is not a trivial task for robot users to create different situations and perform demonstrations under all of them. Therefore, this paper presents a novel concept for learning motion policies from few demonstrations by finding the reference frame weights which capture frame importance/relevance during task executions. Experimental results in both simulation and real robotic environments validate our approach.
翻译:学习从演示(LfD)旨在将人类演示的多才多艺的技能编码。自从它在不需要专业机器人技术知识的情况下促进了知识转移到机器人方面,该领域一直备受欢迎。在任务执行期间,机器人动作通常受到环境所施加的约束的影响。鉴于此,任务参数LfD(TP-LfD)将相关的上下文信息编码到参考帧中,从而使得技能更好地推广到新情况。然而,大多数TP-LfD算法需要在各种环境条件下进行多次演示,以确保有意义的模型具有足够的统计信息。对于机器人用户来说,创建不同的情况并在所有情况下进行演示并不是一项微不足道的任务。因此,本文提出了一种新颖的方法,通过找到参考帧权重来学习少量演示的运动策略,这些权重在任务执行期间捕获框架的重要性/相关性。在仿真和真实机器人环境中的实验结果验证了我们的方法。