PEACH uses a novel spatio-temporal point cloud sequence encoder plus auxiliary supervision to enable zero-shot adaptation of graph network simulators to unseen physical properties, outperforming mesh-based baselines in simulation accuracy while being more deployable for real scenes.
Proceedings of Robotics: Science and Systems (RSS) , year =
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A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.
citing papers explorer
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Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
PEACH uses a novel spatio-temporal point cloud sequence encoder plus auxiliary supervision to enable zero-shot adaptation of graph network simulators to unseen physical properties, outperforming mesh-based baselines in simulation accuracy while being more deployable for real scenes.
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Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.