To provide robots with the flexibility they need to cope with various environments, motion generation techniques using deep learning have been proposed. Generalization in deep learning is expected to enable flexible processing in unknown situations and flexible motion generation. Motion generation models have been proposed to realize specific robot tasks, and their operation successes in unknown situations have been reported. However, their generalization performances have not been analyzed or verified in detail. In this paper, we analyze the internal state of a deep neural network using principal component analysis and verify the generalization of motion against environmental change, specifically a repositioned door knob. The results revealed that the motion primitives were structured in accordance with the position of the door knob. In addition, motion with high generalization performance was obtained by adaptive transition of motion primitives in accordance with changes in the door knob position. The robot was able to successfully perform a door-open-close task at various door knob positions.

中文翻译：

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通过分析机器人运动构造的内部状态来评估视觉运动学习的泛化性能

为了为机器人提供应对各种环境所需的灵活性，已经提出了使用深度学习的运动生成技术。深度学习中的泛化有望实现在未知情况下的灵活处理和灵活的运动生成。已经提出了运动生成模型来实现特定的机器人任务，并且已经报道了它们在未知情况下的操作成功。然而，它们的泛化性能尚未得到详细分析或验证。在本文中，我们使用主成分分析来分析深度神经网络的内部状态，并验证运动对环境变化的概括，特别是重新定位的门把手。结果表明，运动原语是根据门把手的位置构造的。此外，根据门把手位置的变化，通过运动基元的自适应转换获得具有高泛化性能的运动。机器人能够在不同的门把手位置成功地执行开门和关门任务。