Computer Science > Robotics
arXiv:2208.07308 (cs)
[Submitted on 24 Jul 2022]
Title:Pose Forecasting in Industrial Human-Robot Collaboration
Authors:Alessio Sampieri,Guido D'Amely,Andrea Avogaro,Federico Cunico,Geri Skenderi,Francesco Setti,Marco Cristani,Fabio Galasso
View a PDF of the paper titled Pose Forecasting in Industrial Human-Robot Collaboration, by Alessio Sampieri and 7 other authors
View PDFAbstract:Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting. For the first time, SeS-GCN bottlenecks the interaction of the spatial, temporal and channel-wise dimensions in GCNs, and it learns sparse adjacency matrices by a teacher-student framework. Compared to the state-of-the-art, it only uses 1.72% of the parameters and it is ~4 times faster, while still performing comparably in forecasting accuracy on Human3.6M at 1 second in the future, which enables cobots to be aware of human operators. As a second contribution, we present a new benchmark of Cobots and Humans in Industrial COllaboration (CHICO). CHICO includes multi-view videos, 3D poses and trajectories of 20 human operators and cobots, engaging in 7 realistic industrial actions. Additionally, it reports 226 genuine collisions, taking place during the human-cobot interaction. We test SeS-GCN on CHICO for two important perception tasks in robotics: human pose forecasting, where it reaches an average error of 85.3 mm (MPJPE) at 1 sec in the future with a run time of 2.3 msec, and collision detection, by comparing the forecasted human motion with the known cobot motion, obtaining an F1-score of 0.64.
| Comments: | ECCV 2022 |
| Subjects: | Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2208.07308 [cs.RO] |
| (orarXiv:2208.07308v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2208.07308 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Pose Forecasting in Industrial Human-Robot Collaboration, by Alessio Sampieri and 7 other authors
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