Sample efficient learning of manipulation skills poses a major challenge in robotics. While recent approaches demonstrate impressive advances in the type of task that can be addressed and the sensing modalities that can be incorporated, they still require large amounts of training data. Especially with regard to learning actions on robots in the real world, this poses a major problem due to the high costs associated with both demonstrations and real-world robot interactions. To address this challenge, we introduce BOpt-GMM, a hybrid approach that combines imitation learning with own experience collection. We first learn a skill model as a dynamical system encoded in a Gaussian Mixture Model from a few demonstrations. We then improve this model with Bayesian optimization building on a small number of autonomous skill executions in a sparse reward setting. We demonstrate the sample efficiency of our approach on multiple complex manipulation skills in both simulations and real-world experiments. Furthermore, we make the code and pre-trained models publicly available at http://bopt-gmm. cs.uni-freiburg.de.

翻译：样本高效学习操作技能是机器人学面临的主要挑战。尽管近期方法在可处理任务类型和可整合传感模态方面展现出显著进展，但仍需大量训练数据。特别是在真实机器人动作学习方面，由于示范和真实机器人交互的高昂成本，这构成了严峻问题。为应对此挑战，我们提出BOpt-GMM混合方法，将模仿学习与自主经验收集相结合。我们首先通过少量演示学习以高斯混合模型编码动态系统的技能模型，随后基于稀疏奖励场景中少量自主技能执行，利用贝叶斯优化改进该模型。我们通过仿真和真实实验中的多个复杂操作技能验证了方法的样本效率。此外，我们在http://bopt-gmm.cs.uni-freiburg.de公开了代码与预训练模型。