用视觉触觉传感器结合主动采样，提升物体硬度分类效率与准确率。
Investigating Active Sampling for Hardness Classification with Vision-Based Tactile Sensors
- 基于不确定性度量的主动采样策略，动态选择最有信息量的样本。
- 在相同数据上，模型平均准确率达88.78%，远超人类48.00%的水平。
- 适合需要高效触觉感知的机器人抓取与材质识别场景。
硬度是人类和机器人通过触觉感知的重要物体属性。本文研究基于信息论的主动采样策略，以实现视觉触觉传感器下的高效率硬度分类。我们在机器人平台及此前发布的由人类测试者采集的数据集上，评估了三种概率分类器模型和两种基于模型不确定性的采样策略。结果表明，由不确定性驱动的主动采样方法在准确性和稳定性上均优于随机采样基线。此外，在人类测试数据集上，参与者平均准确率为48.00%，而最优方法达到88.78%的平均准确率，充分验证了视觉触觉传感器在物体硬度分类中的有效性。
原文摘要 · Abstract (English)
One of the most important object properties that humans and robots perceive through touch is hardness. This paper investigates information-theoretic active sampling strategies for sample-efficient hardness classification with vision-based tactile sensors. We evaluate three probabilistic classifier models and two model-uncertainty-based sampling strategies on a robotic setup as well as on a previously published dataset of samples collected by human testers. Our findings indicate that the active sampling approaches, driven by uncertainty metrics, surpass a random sampling baseline in terms of accuracy and stability. Additionally, while in our human study, the participants achieve an average accuracy of 48.00%, our best approach achieves an average accuracy of 88.78% on the same set of objects, demonstrating the effectiveness of vision-based tactile sensors for object hardness classification.
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