Adaptation and Robust Learning of Probabilistic Movement Primitives
Adaptation and Robust Learning of Probabilistic Movement Primitives
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DOI：
10.1109/tro.2019.2937010
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发表时间：
2020-04-01
影响因子：
7.8
通讯作者：
Peters, Jan
中科院分区：
文献类型：
--
作者：
Gomez-Gonzalez, Sebastian;Neumann, Gerhard;Peters, Jan
运动基元的概率表示为机器人技术中的机器学习开辟了重要的新可能性。这些表示能够捕获教师演示的可变性作为轨迹上的概率分布，提供合理的探索区域和适应机器人环境变化的能力。然而，为了能够捕获不同关节之间的可变性和相关性，与其确定性对应物相比，概率性运动基元需要估计更多数量的参数，其仅专注于对平均行为进行建模。在这篇文章中，我们利用先验分布的概率运动原语的参数进行鲁棒估计的参数与几个训练实例。此外，我们引入了通用的运营商，以适应运动基元在联合和任务空间。建议的训练方法和适应运营商进行了测试，在咖啡准备和机器人乒乓球任务。在咖啡制备任务中，我们评估了目标区域中咖啡研磨机和冲泡室的位置变化的泛化性能，仅在两次演示后就实现了所需的行为。在乒乓球任务中，我们评估了命中率和返回率，优于以前的方法，同时使用更少的任务特定策略。
Probabilistic representations of movement primitives open important new possibilities for machine learning in robotics. These representations are able to capture the variability of the demonstrations from a teacher as a probability distribution over trajectories, providing a sensible region of exploration and the ability to adapt to changes in the robot environment. However, to be able to capture variability and correlations between different joints, a probabilistic movement primitive requires the estimation of a larger number of parameters compared to their deterministic counterparts, which focus on modeling only the mean behavior. In this article, we make use of prior distributions over the parameters of a probabilistic movement primitive to make robust estimates of the parameters with few training instances. In addition, we introduce general purpose operators to adapt movement primitives in joint and task space. The proposed training method and adaptation operators are tested in a coffee preparation and in robot table tennis task. In the coffee preparation task we evaluate the generalization performance to changes in the location of the coffee grinder and brewing chamber in a target area, achieving the desired behavior after only two demonstrations. In the table tennis task we evaluate the hit and return rates, outperforming previous approaches while using fewer task specific heuristics.