Swing anomaly detection for robotic manipulation of objects

Abstract

Techniques described herein relate to swing anomaly detection for objects transferred via robotic manipulation. For example, a computer system can generate, based on first sensor data of a robotic manipulator manipulating an object and by using a machine learning model, a first segmentation of the object represented by the first sensor data. The first sensor data can be received at a first time. The computer system can generate, based on second sensor data of the robotic manipulator manipulating the object and by using the machine learning model, a second segmentation of the object represented by the second sensor data. The computer system can generate, by comparing the first segmentation and the second segmentation, a swinging score indicating swinging of the object by the robotic manipulator. A motion plan for the robotic manipulator can be updated based on the swinging score.