Neurosymbolic framework grounds skeleton motion in learnable pose and dynamics concepts then reasons over them with differentiable logic to recognize actions interpretably on NTU and NW-UCLA benchmarks.
arXiv preprint arXiv:2505.05519 , year=
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User studies reveal preferences for visual abstractions and distance-dependent low-resolution capture, leading to a configurable privacy policy for robot navigation.
Embodied AI requires treating privacy as a lifecycle architectural constraint rather than a stage-local feature, addressed via the proposed SPINE framework with a multi-criterion privacy classification matrix.
citing papers explorer
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Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition
Neurosymbolic framework grounds skeleton motion in learnable pose and dynamics concepts then reasons over them with differentiable logic to recognize actions interpretably on NTU and NW-UCLA benchmarks.
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Designing Privacy-Preserving Visual Perception for Robot Navigation Based on User Privacy Preferences
User studies reveal preferences for visual abstractions and distance-dependent low-resolution capture, leading to a configurable privacy policy for robot navigation.
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Position: Embodied AI Requires a Privacy-Utility Trade-off
Embodied AI requires treating privacy as a lifecycle architectural constraint rather than a stage-local feature, addressed via the proposed SPINE framework with a multi-criterion privacy classification matrix.