InfBaGel generates consistent human-object-scene interactions via dynamic perception during iterative refinement in a consistency model, bump-aware guidance to avoid collisions, and hybrid training that mixes synthesized pseudo-samples with real HSI data.
Evolvinggrasp: Evo- lutionary grasp generation via efficient preference alignment
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HVG-3D uses a 3D-aware diffusion architecture with ControlNet to synthesize high-fidelity hand-object interaction videos from 3D control signals, achieving state-of-the-art spatial fidelity and temporal coherence on the TASTE-Rob dataset.
FastGrasp uses two-stage RL with CVAE for diverse grasp candidates from point clouds and tactile sensing for impact adjustments to achieve robust fast whole-body grasping in sim and real-world settings.
citing papers explorer
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InfBaGel: Human-Object-Scene Interaction Generation with Dynamic Perception and Iterative Refinement
InfBaGel generates consistent human-object-scene interactions via dynamic perception during iterative refinement in a consistency model, bump-aware guidance to avoid collisions, and hybrid training that mixes synthesized pseudo-samples with real HSI data.
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HVG-3D: Bridging Real and Simulation Domains for 3D-Conditional Hand-Object Interaction Video Synthesis
HVG-3D uses a 3D-aware diffusion architecture with ControlNet to synthesize high-fidelity hand-object interaction videos from 3D control signals, achieving state-of-the-art spatial fidelity and temporal coherence on the TASTE-Rob dataset.
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FastGrasp: Learning-based Whole-body Control method for Fast Dexterous Grasping with Mobile Manipulators
FastGrasp uses two-stage RL with CVAE for diverse grasp candidates from point clouds and tactile sensing for impact adjustments to achieve robust fast whole-body grasping in sim and real-world settings.