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SceneSmith generates realistic indoor scenes for robot simulation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SceneSmith, a novel agentic framework designed to generate realistic indoor environments for robot training simulations. This system uses a hierarchical approach with interacting VLM agents to create scenes from natural language prompts, incorporating asset generation and physical property estimation. SceneSmith produces significantly more objects than previous methods with minimal collisions and high physical stability, achieving high realism and prompt faithfulness in user studies. AI

IMPACT Enables more diverse and complex training environments for home robots, potentially accelerating their development and deployment.

RANK_REASON The cluster contains an academic paper detailing a new method for generating simulation environments. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SceneSmith generates realistic indoor scenes for robot simulation

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The cluster contains an academic paper detailing a new method for generating simulation environments. [lever_c_demoted from research: ic=1 ai=1.0]
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116 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nicholas Pfaff, Thomas Cohn, Sergey Zakharov, Rick Cory, Russ Tedrake ·

    SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes

    arXiv:2602.09153v2 Announce Type: replace-cross Abstract: Simulation has become a key tool for training and evaluating home robots at scale, yet existing environments fail to capture the diversity and physical complexity of real indoor spaces. Current scene synthesis methods prod…

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