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.

中文翻译：

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概率运动原语的适应和鲁棒学习


运动基元的概率表示为机器人领域的机器学习开辟了重要的新可能性。这些表示能够将教师演示的可变性捕获为轨迹上的概率分布，从而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变异性和相关性，与确定性运动原语相比，概率运动原语需要估计更多的参数，而确定性运动原语专注于仅对平均行为进行建模。在本文中，我们利用概率运动基元参数的先验分布，通过很少的训练实例对参数进行稳健估计。此外，我们引入了通用算子来适应关节和任务空间中的运动原语。所提出的训练方法和适应算子在咖啡制备和机器人乒乓球任务中进行了测试。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，我们评估命中率和返回率，优于以前的方法，同时使用更少的特定于任务的启发法。