X-Capture: An Open-Source Portable Device for Multi-Sensory Learning

• URL: http://arxiv.org/abs/2504.02318v1
• Date: Thu, 03 Apr 2025 06:44:25 GMT
• Title: X-Capture: An Open-Source Portable Device for Multi-Sensory Learning
• Authors: Samuel Clarke, Suzannah Wistreich, Yanjie Ze, Jiajun Wu,
• Abstract summary: We introduce X-Capture, an open-source, portable, and cost-effective device for real-world multi-sensory data collection.<n>With a build cost under $1,000, X-Capture democratizes the creation of multi-sensory datasets.<n>X-Capture lays the groundwork for advancing human-like sensory representations in AI.
• Score: 11.632896115888261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding objects through multiple sensory modalities is fundamental to human perception, enabling cross-sensory integration and richer comprehension. For AI and robotic systems to replicate this ability, access to diverse, high-quality multi-sensory data is critical. Existing datasets are often limited by their focus on controlled environments, simulated objects, or restricted modality pairings. We introduce X-Capture, an open-source, portable, and cost-effective device for real-world multi-sensory data collection, capable of capturing correlated RGBD images, tactile readings, and impact audio. With a build cost under $1,000, X-Capture democratizes the creation of multi-sensory datasets, requiring only consumer-grade tools for assembly. Using X-Capture, we curate a sample dataset of 3,000 total points on 500 everyday objects from diverse, real-world environments, offering both richness and variety. Our experiments demonstrate the value of both the quantity and the sensory breadth of our data for both pretraining and fine-tuning multi-modal representations for object-centric tasks such as cross-sensory retrieval and reconstruction. X-Capture lays the groundwork for advancing human-like sensory representations in AI, emphasizing scalability, accessibility, and real-world applicability.

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