DeSeG decouples semantic intent from geometric constraints in human-scene interaction synthesis using a residual CVAE planner and a physics-regularized diffusion executor, reducing scene penetration by 47% and improving semantic alignment by 29% over SOTA baselines on the Lingo dataset.
Collision-free humanoid traversal in cluttered indoor scenes
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
A data-centric approach shows that less than 3% of AMASS motion data, filtered by physics feasibility, diversity, and complexity, yields better humanoid tracking policies than the full dataset.
ConstrainedMimic integrates operational space control and control barrier functions into RL tracking policies to enforce arbitrary runtime constraints on humanoid kinematics and dynamics while preserving contact modes and tracking goals.
Raw proximity measurements can substitute for explicit object localization in humanoid collision avoidance if sensing range is sufficient, and sparse non-directional proximity signals train more efficiently than dense directional alternatives.
citing papers explorer
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DeSeG: Decoupling Semantic Intent and Geometric Constraints for Physically Plausible Human-Scene Interaction
DeSeG decouples semantic intent from geometric constraints in human-scene interaction synthesis using a residual CVAE planner and a physics-regularized diffusion executor, reducing scene penetration by 47% and improving semantic alignment by 29% over SOTA baselines on the Lingo dataset.
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LIMMT: Less is More for Motion Tracking
A data-centric approach shows that less than 3% of AMASS motion data, filtered by physics feasibility, diversity, and complexity, yields better humanoid tracking policies than the full dataset.
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Constrained Whole-Body Tracking for Humanoid Robots
ConstrainedMimic integrates operational space control and control barrier functions into RL tracking policies to enforce arbitrary runtime constraints on humanoid kinematics and dynamics while preserving contact modes and tracking goals.
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Egocentric Tactile and Proximity Sensors as Observation Priors for Humanoid Collision Avoidance
Raw proximity measurements can substitute for explicit object localization in humanoid collision avoidance if sensing range is sufficient, and sparse non-directional proximity signals train more efficiently than dense directional alternatives.