DeepSoMo: Deformable Object Motion Estimation under Proprioceptive Partial Observability via Predictive Latent Dynamics
Overview
This research introduces DeepSoMo, a framework that enforces predictive structure in latent dynamics through self-supervised constraints to estimate motion of deformable bodies from proprioceptive sensing. Evaluated in high-fidelity finite-element simulations, the approach demonstrates consistent improvements in trajectory estimation, orientation recovery, shape reconstruction, and contact inference compared to recurrent baselines.
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- arXiv Robotics — research abstractsSignalDeepSoMo: Deformable Object Motion Estimation under Proprioceptive Partial Observability via Predictive Latent Dynamics
This research introduces DeepSoMo, a framework that enforces predictive structure in latent dynamics through self-supervised constraints to estimate motion of deformable bodies from proprioceptive sensing. Evaluated in high-fidelity finite-element simulations, the approach demonstrates consistent improvements in trajectory estimation, orientation recovery, shape reconstruction, and contact inference compared to recurrent baselines.
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