An Artificial Intelligence Approach for Interpreting Creative Combinational Designs

Combinational creativity, a form of creativity involving the blending of familiar ideas, is pivotal in design innovation. While most research focuses on how combinational creativity in design is achieved through blending elements, this study focuses on the computational interpretation, specifically identifying the 'base' and 'additive' components that constitute a creative design. To achieve this goal, the authors propose a heuristic algorithm integrating computer vision and natural language processing technologies, and implement multiple approaches based on both discriminative and generative artificial intelligence architectures. A comprehensive evaluation was conducted on a dataset created for studying combinational creativity. Among the implementations of the proposed algorithm, the most effective approach demonstrated a high accuracy in interpretation, achieving 87.5% for identifying 'base' and 80% for 'additive'. We conduct a modular analysis and an ablation experiment to assess the performance of each part in our implementations. Additionally, the study includes an analysis of error cases and bottleneck issues, providing critical insights into the limitations and challenges inherent in the computational interpretation of creative designs.

Towards the Neuromorphic Computing for Offroad Robot Environment Perception and Navigation

My research objective is to explicitly bridge the gap between high computational performance and low power dissipation of robot on-board hardware by designing a bio-inspired tapered whisker neuromorphic computing (also called reservoir computing) system for offroad robot environment perception and navigation, that centres the interaction between a robot's body and its environment. Mobile robots performing tasks in unknown environments need to traverse a variety of complex terrains, and they must be able to reliably and quickly identify and characterize these terrains to avoid getting into potentially challenging or catastrophic circumstances. To solve this problem, I drew inspiration from animals like rats and seals, just relying on whiskers to perceive surroundings information and survive in dark and narrow environments. Additionally, I looked to the human cochlear which can separate different frequencies of sound. Based on these insights, my work addresses this need by exploring the physical whisker-based reservoir computing for quick and cost-efficient mobile robots environment perception and navigation step by step. This research could help us understand how the compliance of the biological counterparts helps robots to dynamically interact with the environment and provides a new solution compared with current methods for robot environment perception and navigation with limited computational resources, such as Mars.