机器人自我表现提高了操作技巧和转移学习

冯D.H. Nguyen， 曼弗雷德·埃佩， 斯特凡·韦尔姆特

认知科学表明，自我表现对于学习和解决问题至关重要。然而，目前缺乏计算方法将这种说法与认知上合理的机器人和强化学习联系起来。本文通过开发一种模型来学习双向动作效应关联，从多感官信息（名为多式联运 BidAL）中对体型架构的表示和近位空间进行编码，从而弥补这一差距。通过三种不同的机器人实验，我们证明了这种方法可以显著稳定在嘈杂条件下基于学习的问题解决，并改善机器人操作技能的转移学习。

Robotic self-representation improves manipulation skills and transfer learning

Phuong D.H. Nguyen, Manfred Eppe, Stefan Wermter

Cognitive science suggests that the self-representation is critical for learning and problem-solving. However, there is a lack of computational methods that relate this claim to cognitively plausible robots and reinforcement learning. In this paper, we bridge this gap by developing a model that learns bidirectional action-effect associations to encode the representations of body schema and the peripersonal space from multisensory information, which is named multimodal BidAL. Through three different robotic experiments, we demonstrate that this approach significantly stabilizes the learning-based problem-solving under noisy conditions and that it improves transfer learning of robotic manipulation skills.