This dissertation explores minimalist perception, a design philosophy prioritizing simplicity and efficiency in sensor technology to capture essential information for various applications, including robotics, environmental monitoring, and wearable technology. By focusing on streamlined functionalities, these sensors avoid the complexities and costs of more elaborate systems, offering practical solutions under resource constraints. Our research emphasizes developing low-power, miniaturized systems that integrate seamlessly into both urban and natural environments, enhancing ubiquitous perception without the encumbrance of resource constraints.  We explore three main areas: low-power and miniaturized acoustic direction-of-arrival (DoA) estimation, ultra-low-power spatial sensing for miniature robots, and a single frequency-based tracking interface for voice assistants. The contributions include a novel low-power DoA estimation system using 3D-printed metamaterials, an innovative spatial sensing system for mobile robots using a single speaker-microphone pair, and a comprehensive voice and motion tracking interface that operates on a single frequency. Moreover, the integration with LLM demonstrates how minimalist sensing can enrich AI models with spatial perception capabilities, opening new possibilities for human-centered AI applications.