Object slip perception is essential for mobile manipulation robots to perform manipulation tasks reliably in the dynamic real-world. Traditional approaches to robot arms' slip perception use tactile or vision sensors. However, mobile robots still have to deal with noise in their sensor signals caused by the robot's movement in a changing environment. To solve this problem, we present an anomaly detection method that utilizes multisensory data based on a deep autoencoder model. The proposed framework integrates heterogeneous data streams collected from various robot sensors, including RGB and depth cameras, a microphone, and a force-torque sensor. The integrated data is used to train a deep autoencoder to construct latent representations of the multisensory data that indicate the normal status. Anomalies can then be identified by error scores measured by the difference between the trained encoder's latent values and the latent values of reconstructed input data. In order to evaluate the proposed framework, we conducted an experiment that mimics an object slip by a mobile service robot operating in a real-world environment with diverse household objects and different moving patterns. The experimental results verified that the proposed framework reliably detects anomalies in object slip situations despite various object types and robot behaviors, and visual and auditory noise in the environment.

翻译：物体滑移感知对于移动操作机器人在动态真实环境中可靠执行操作任务至关重要。传统机器人手臂的滑移感知方法通常依赖触觉或视觉传感器。然而，移动机器人仍需应对由机器人在变化环境中移动所导致的传感器信号噪声。为解决这一问题，我们提出一种基于深度自编码器模型、利用多感官数据的异常检测方法。该框架整合了从机器人各种传感器（包括RGB与深度相机、麦克风及力觉传感器）采集的异构数据流。整合后的数据用于训练深度自编码器，以构建表征正常状态的多感官数据潜在表示。随后，可通过训练后的编码器潜在值与重构输入数据潜在值之间的差异所计算出的误差分数来识别异常。为评估所提框架，我们设计了一项实验，模拟移动服务机器人在真实环境中操作不同家庭物体及不同运动模式时发生的物体滑移情况。实验结果表明，尽管物体类型与机器人行为多样，且存在视觉与听觉环境噪声，该框架仍能可靠检测物体滑移情境下的异常。