PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification 事件

PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification arXiv:2606.04226v1 Announce Type: cross Abstract: 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 pipe