A 48-camera residential platform delivers real-time occlusion-robust 3D perception and coordinated actuation for multi-human multi-robot interaction in a shared home workspace.
48550/arXiv.2504.10414
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
dataset 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4representative citing papers
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.
IMU-to-4D uses wearable IMU data and repurposed LLMs to predict coherent 4D human motion plus coarse scene structure, outperforming cascaded state-of-the-art pipelines in temporal stability.
GRAIL creates over 20,000 synthetic loco-manipulation sequences from known 3D configurations and video priors, then trains policies that achieve 84% pick-up and 90% stair-climbing success on a real Unitree G1 humanoid using only the generated data.
citing papers explorer
-
OmniRobotHome: A Multi-Camera Platform for Real-Time Multiadic Human-Robot Interaction
A 48-camera residential platform delivers real-time occlusion-robust 3D perception and coordinated actuation for multi-human multi-robot interaction in a shared home workspace.
-
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.
-
Seeing Without Eyes: 4D Human-Scene Understanding from Wearable IMUs
IMU-to-4D uses wearable IMU data and repurposed LLMs to predict coherent 4D human motion plus coarse scene structure, outperforming cascaded state-of-the-art pipelines in temporal stability.
-
GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors
GRAIL creates over 20,000 synthetic loco-manipulation sequences from known 3D configurations and video priors, then trains policies that achieve 84% pick-up and 90% stair-climbing success on a real Unitree G1 humanoid using only the generated data.