用声音触觉感知实现复杂抓取，仅靠声音就能判断物体状态和接触类型。
VibeCheck: Using Active Acoustic Tactile Sensing for Contact-Rich Manipulation
- 用压电手指主动发射接收声波，感知物体声学特性与接触状态。
- 可识别物体材质、内部结构姿态、外部接触类型，并用于插销任务。
- 适合需要无视觉反馈的机器人操作场景，如黑暗或遮挡环境。
物体的声学响应能揭示其整体状态，例如材料属性或与外界的外部接触。本文构建了一个配备两个压电手指的主动声学感知夹爪：一个用于生成信号，另一个用于接收。通过将声波从一个手指传递到另一个手指，经由物体传导，我们得以获取物体的声学特性及其接触状态信息。利用该系统，可实现物体分类、抓取位置估计、内部结构姿态估计，以及外部接触类型的分类。基于接触类型分类模型，解决了一个标准的长时程操作任务——插销。采用基于传感器性能的简单模拟转移模型，训练出对分类器预测不准确具有鲁棒性的模仿学习策略。最终在UR5机器人上仅使用主动声学感知作为唯一反馈，验证了该策略的有效性。视频见 https://roamlab.github.io/vibecheck。
原文摘要 · Abstract (English)
The acoustic response of an object can reveal a lot about its global state, for example its material properties or the extrinsic contacts it is making with the world. In this work, we build an active acoustic sensing gripper equipped with two piezoelectric fingers: one for generating signals, the other for receiving them. By sending an acoustic vibration from one finger to the other through an object, we gain insight into an object's acoustic properties and contact state. We use this system to classify objects, estimate grasping position, estimate poses of internal structures, and classify the types of extrinsic contacts an object is making with the environment. Using our contact type classification model, we tackle a standard long-horizon manipulation problem: peg insertion. We use a simple simulated transition model based on the performance of our sensor to train an imitation learning policy that is robust to imperfect predictions from the classifier. We finally demonstrate the policy on a UR5 robot with active acoustic sensing as the only feedback. Videos can be found at https://roamlab.github.io/vibecheck .
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