PerceptTwin creates interactive simulations from open-vocabulary object maps for verifying and refining LLM robot plans, reporting ~39% higher success rates and up to 18% better human verification.
Delta: Decomposed efficient long-term robot task planning using large lan- guage models,
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STPR uses LLMs to generate Python constraint functions from natural language instructions, then applies them via traditional search algorithms to point clouds in simulated Gazebo robot environments with reported full compliance.
citing papers explorer
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PerceptTwin: Semantic Scene Reconstruction for Iterative LLM Planning and Verification
PerceptTwin creates interactive simulations from open-vocabulary object maps for verifying and refining LLM robot plans, reporting ~39% higher success rates and up to 18% better human verification.
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"Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation
STPR uses LLMs to generate Python constraint functions from natural language instructions, then applies them via traditional search algorithms to point clouds in simulated Gazebo robot environments with reported full compliance.