[ICRA 2025] We present a Morphology-Informed Heterogeneous Graph Neural Network (MI-HGNN) for learning-based contact perception. The architecture and connectivity of the MI-HGNN are constructed from the robot morphology.
Dynamically discovering metapaths, required for processing in various Heterogeneous graph neural network models. Eliminating the need of manual metapath providing. Ranking metapaths based on the downstream task to get the best metapaths intended towards the downstream task.
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