论文标题
机器人自我代表提高了操纵技巧和转移学习
Robotic self-representation improves manipulation skills and transfer learning
论文作者
论文摘要
认知科学表明，自我代表对于学习和解决问题至关重要。但是，缺乏将这种主张与认知合理的机器人和增强学习联系起来的计算方法。在本文中，我们通过开发一个模型来弥合这一差距，该模型学习双向动作效应关联，以编码从多感官信息中的人体模式的表示和人周围空间，该信息被称为多模式bidal。通过三个不同的机器人实验，我们证明了这种方法在嘈杂条件下显着稳定基于学习的问题，并改善了机器人操纵技能的转移学习。
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.
