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Library to build temporal task models from bimanual manipulation data.
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Python bindings for Simox (http://simox.sourceforge.net/), generated using SWIG.
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Fork of https://github.com/epfl-lasa/dynamic_obstacle_avoidance_cpp used for the dynamic obstacle avoidance
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Python utility module to work with the KIT Bimanual Actions Dataset
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ArmarX package of the 3-stage pipeline to extract scene graphs from RGB-D images
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Graph network based bimanual action classifier
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Source code of the Motion Annotation Tool hosted at https://motion-annotation.humanoids.kit.edu/.
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Source code for our paper "Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks", Plappert et al. 2017. Pre-print is available here: https://arxiv.org/abs/1705.06400
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IMAGINE Documentation GitLab Pages: https://h2t.gitlab.io/imagine-documentation/
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