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：运动基元是一种可训练的参数化模型，能够基于有限数量的演示样本复现机器人运动。先前的研究提出了简单的线性模型，这些模型通过允许运动的时间调制（以更快或更慢的速度复现运动）、混合（将两个运动合并为一个）、经由点条件约束（强制运动通过特定经由点）以及上下文条件约束（基于观测变量生成运动，例如物体位置），展现出高样本效率与泛化能力。已有研究提出了基于神经网络的动作基元模型，并证明了其能够执行具有某种形式输入条件或时间调制表示的任务。然而，目前尚未提出一个能够统一实现所有上述操作的深度动作基元模型，这限制了神经动作基元的潜在应用。本文提出了一种深度运动基元架构，该架构编码了所有上述操作，并采用贝叶斯上下文聚合器以实现更合理的上下文条件约束与混合。实验结果表明，与基线方法相比，我们的方法能够扩展到更广泛的输入选择上复现复杂运动，同时保持了线性运动基元所提供的各项操作。