Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations. Previous works proposed simple linear models that exhibited high sample efficiency and generalization power by allowing temporal modulation of movements (reproducing movements faster or slower), blending (merging two movements into one), via-point conditioning (constraining a movement to meet some particular via-points) and context conditioning (generation of movements based on an observed variable, e.g., position of an object). Previous works have proposed neural network-based motor primitive models, having demonstrated their capacity to perform tasks with some forms of input conditioning or time-modulation representations. However, there has not been a single unified deep motor primitive's model proposed that is capable of all previous operations, limiting neural motor primitive's potential applications. This paper proposes a deep movement primitive architecture that encodes all the operations above and uses a Bayesian context aggregator that allows a more sound context conditioning and blending. Our results demonstrate our approach can scale to reproduce complex motions on a larger variety of input choices compared to baselines while maintaining operations of linear movement primitives provide.

翻译：运动基元是可训练的参量化模型，能够从有限的示范中复现机器人运动。以往研究提出了简单的线性模型，通过允许运动的时间调制（加快或减慢复现速度）、融合（将两个运动合并为一个）、路点约束（强制运动经过特定中间点）以及情境条件化（基于观测变量如物体位置生成运动），展现了高样本效率和泛化能力。现有工作已提出基于神经网络的运动基元模型，证明了其具备执行某些输入条件化或时间调制表征任务的能力。然而，尚未出现能够统一执行所有上述操作的深度运动基元模型，限制了神经运动基元的潜在应用。本文提出了一种深度运动基元架构，该架构编码了上述所有操作，并采用贝叶斯情境聚合器，实现了更合理的情境条件化与融合。实验结果表明，与基线方法相比，本方法能够在更大范围的输入选择上扩展并复现复杂运动，同时保持线性运动基元的操作特性。