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模型，能从有限的演示集中复现机器人运动。先前的工作通过允许运动的时间调制（加快或减慢复现速度）、混合（合并两个动作为一个）、途经点约束（迫使运动通过特定途经点）以及环境条件约束（基于观察变量如物体位置生成运动），提出了展现出高样本效率和泛化能力的简单线性模型。已有研究提出了基于神经网络的运动基元模型，证实了其通过某种形式的输入条件或时间调制表征来执行任务的能力。然而，目前尚未有统一的深度运动基元模型能兼容所有先前的操作，这限制了神经运动基元的潜在应用。本文提出一种深度运动基元架构，该架构编码了上述所有操作，并采用贝叶斯环境聚合器实现更严谨的环境条件约束与混合。实验结果表明，与基线方法相比，我们的方法在维持线性运动基元操作特性的同时，能够扩展到在更广泛的输入选择上复现复杂运动。