Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks

Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot

We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resulting audio signals from microphones that are embedded into the robotic ears. A neural network architecture learns from these signals to analyze properties of the contents of the containers. Specifically, we evaluate the material classification and weight prediction accuracy and demonstrate that the framework is fairly robust to acoustic real-world noise.

Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool

Sketches are abstract representations of visual perception and visuospatial construction. In this work, we proposed a new framework, Generative Adversarial Networks with Conditional Neural Movement Primitives (GAN-CNMP), that incorporates a novel adversarial loss on CNMP to increase sketch smoothness and consistency. Through the experiments, we show that our model can be trained with few unlabeled samples, can construct distributions automatically in the latent space, and produces better results than the base model in terms of shape consistency and smoothness.

通过交互式质量多样性实现设计空间的可控探索

Controllable Exploration of a Design Space via Interactive Quality Diversity

本文提出一种基于质量多样性的用户驱动进化算法，通过在行为空间小窗口内采样备选方案并随用户选择动态调整窗口，有效降低了用户认知负荷与疲劳，在建筑布局生成任务中比 MAP-Elites 找到了更符合用户偏好的解决方案。