RAM is a morphology-conditioned implicit neural representation trained on 3e10 forward-kinematics samples that serves as a fast, differentiable surrogate for pose reachability and generalizes to unseen morphologies while accounting for self-collisions.
Neural fields in robotics: A survey
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PhysGraph reconstructs object-centric 3D geometry from RGB-D, decomposes objects into parts, infers materials and articulations via visual reasoning, and reports SOTA results on semantic segmentation, multi-object mass estimation, and articulation prediction.
ODE-GS uses latent neural ODEs on Gaussian parameters to extrapolate dynamic 3D scenes, reporting 19.8% metric gains over baselines on D-NeRF, NVFi, and HyperNeRF.
citing papers explorer
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RAM: Reachability Across Morphologies
RAM is a morphology-conditioned implicit neural representation trained on 3e10 forward-kinematics samples that serves as a fast, differentiable surrogate for pose reachability and generalizes to unseen morphologies while accounting for self-collisions.
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PhysGraph: A Physics-aware 3D Scene Graph for Perception and Reasoning
PhysGraph reconstructs object-centric 3D geometry from RGB-D, decomposes objects into parts, infers materials and articulations via visual reasoning, and reports SOTA results on semantic segmentation, multi-object mass estimation, and articulation prediction.