用神经隐式函数压缩触觉数据，提升泛化与推理能力。
Tactile Functasets: Neural Implicit Representations of Tactile Datasets
- 用神经隐式函数重构触觉数据，替代原始图像。
- 压缩数据体积，提升跨传感器泛化性能。
- 适合触觉感知与机器人抓取研究者使用。
现代触觉传感器生成高维原始感官反馈（如图像），导致存储、处理和跨传感器泛化困难。为解决此问题，本文提出一种触觉数据的神经隐式表示方法。不直接使用原始触觉图像，而是训练神经隐式函数以重建触觉数据集，生成紧凑的表示，捕捉感官输入的潜在结构。该表示相比原始数据具有多重优势：数据紧凑，支持概率可解释推理，并促进跨传感器泛化。我们在手内物体位姿估计任务上验证了该方法的有效性，在性能上优于基于图像的方法，同时简化了下游模型。代码、演示及数据集已公开于 https://www.mmintlab.com/tactile-functasets。
原文摘要 · Abstract (English)
Modern incarnations of tactile sensors produce high-dimensional raw sensory feedback such as images, making it challenging to efficiently store, process, and generalize across sensors. To address these concerns, we introduce a novel implicit function representation for tactile sensor feedback. Rather than directly using raw tactile images, we propose neural implicit functions trained to reconstruct the tactile dataset, producing compact representations that capture the underlying structure of the sensory inputs. These representations offer several advantages over their raw counterparts: they are compact, enable probabilistically interpretable inference, and facilitate generalization across different sensors. We demonstrate the efficacy of this representation on the downstream task of in-hand object pose estimation, achieving improved performance over image-based methods while simplifying downstream models. We release code, demos and datasets at https://www.mmintlab.com/tactile-functasets.
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