専門家の積による模倣学習：マニピュレーションとタスク優先順位付けへの応用
Learning from demonstration using products of experts: applications to manipulation and task prioritization
複数のタスク空間の確率分布を専門家の積（PoE）として統合し、変分推論で効率的に学習する手法を提案。競合・階層的なタスクやナルスペース構造への拡張も示す。
著者: Emmanuel Pignat, João Silvério, Sylvain Calinon
分類: cs.RO, cs.LG
原文アブストラクト
Probability distributions are key components of many learning from demonstration (LfD) approaches. While the configuration of a manipulator is defined by its joint angles, poses are often best explained within several task spaces. In many approaches, distributions within relevant task spaces are learned independently and only combined at the control level. This simplification implies several problems that are addressed in this work. We show that the fusion of models in different task spaces can be expressed as a product of experts (PoE), where the probabilities of the models are multiplied and renormalized so that it becomes a proper distribution of joint angles. Multiple experiments are presented to show that learning the different models jointly in the PoE framework significantly improves the quality of the model. The proposed approach particularly stands out when the robot has to learn competitive or hierarchical objectives. Training the model jointly usually relies on contrastive divergence, which requires costly approximations that can affect performance. We propose an alternative strategy using variational inference and mixture model approximations. In particular, we show that the proposed approach can be extended to PoE with a nullspace structure (PoENS), where the model is able to recover tasks that are masked by the resolution of higher-level objectives.
