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arXiv · 2206.12612

Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions

Weilin Wan·Lei Yang·Lingjie Liu·Zhuoying Zhang·Ruixing Jia·Yi-King Choi·Jia Pan·Christian Theobalt·Taku Komura·Wenping Wang
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Abstract

Understanding human intentions during interactions has been a long-lasting theme, that has applications in human-robot interaction, virtual reality and surveillance. In this study, we focus on full-body human interactions with large-sized daily objects and aim to predict the future states of objects and humans given a sequential observation of human-object interaction. As there is no such dataset dedicated to full-body human interactions with large-sized daily objects, we collected a large-scale dataset containing thousands of interactions for training and evaluation purposes. We also observe that an object's intrinsic physical properties are useful for the object motion prediction, and thus design a set of object dynamic descriptors to encode such intrinsic properties. We treat the object dynamic descriptors as a new modality and propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task. We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network better generalize to unseen objects. We also demonstrate the predicted results are useful for human-robot collaborations.

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Weilin Wan, Lei Yang, Lingjie Liu, Zhuoying Zhang, Ruixing Jia, Yi-King Choi, Jia Pan, Christian Theobalt, Taku Komura, Wenping Wang. 2022-06-25. Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions. https://doi.org/10.1109/lra.2022.3151614

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