PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification
Simulation environments are useful for both robot policy learning and planning verification and validation. Traditionally, the process of creating a simulation was onerous. Creating a bespoke simulation environment for each individual environment that a robot would operate in was simply infeasible. In this work, we introduce PerceptTwin, a fully automatic pipeline that constructs interactive simulations directly from semantic scene representations produced by a robot's perception stack. PerceptT
Record details
Published: 2 June 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (2 June 2026), “PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification,” evidence record 3186, https://ethics.ai/record/3186 (originally published by arXiv).
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