论文标题
检测，拒绝，正确：损坏传感器的跨模式补偿
Detect, Reject, Correct: Crossmodal Compensation of Corrupted Sensors
论文作者
论文摘要
使用来自多种模式的传感器数据为编码冗余和互补功能提供了机会，当一种模式损坏或嘈杂时，这些功能可能很有用。人类每天都这样做，依靠视觉挑战环境中的触摸和本体感受反馈。但是，机器人可能并不总是知道何时损坏其传感器，因为即使是损坏的传感器也可以返回有效的值。在这项工作中，我们介绍了Crossmodal补偿模型（CCM），该模型可以检测损坏的传感器模式并补偿它们。 CMM是一个以自学意义的代表模型，该模型利用单峰重建损失用于腐败检测。然后，CCM将损坏的模式丢弃，并通过其余传感器的信息来补偿它。我们表明，CCM学习丰富的状态表示形式，即使输入方式以训练时间未见的方式损坏了接触式的操纵政策，即使输入方式被损坏。
Using sensor data from multiple modalities presents an opportunity to encode redundant and complementary features that can be useful when one modality is corrupted or noisy. Humans do this everyday, relying on touch and proprioceptive feedback in visually-challenging environments. However, robots might not always know when their sensors are corrupted, as even broken sensors can return valid values. In this work, we introduce the Crossmodal Compensation Model (CCM), which can detect corrupted sensor modalities and compensate for them. CMM is a representation model learned with self-supervision that leverages unimodal reconstruction loss for corruption detection. CCM then discards the corrupted modality and compensates for it with information from the remaining sensors. We show that CCM learns rich state representations that can be used for contact-rich manipulation policies, even when input modalities are corrupted in ways not seen during training time.
