LIVIN: Benchmarking Spatial and Embodied Intelligence in Digital Twins of Lived-In Homes

Key points

• Realistic household simulation must capture not only diverse environments but also the lived-in object arrangements and spatial constraints that shape robot motion and interaction.1
• We evaluate four tasks in LIVIN: 3D detection, 3D reconstruction, navigation, and loco-manipulation.1
• Our evaluations show that current methods remain challenged by the dense object arrangements, occlusions, limited free space, and constrained interaction regions found in realistic lived-in homes.1
• We hope LIVIN will help advance embodied AI in real-world homes, from spatial understanding to robotic interaction, and ultimately bring embodied intelligence into everyday home environments.1

Sources (1)

• [1]LIVIN: Benchmarking Spatial and Embodied Intelligence in Digital Twins of Lived-In Homes
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 02:47 PM

  To this end, we introduce LIVIN, a benchmark for spatial and embodied intelligence built on digital twins of 30 diverse lived-in homes.

  Realistic household simulation must capture not only diverse environments but also the lived-in object arrangements and spatial constraints that shape robot motion and interaction.

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