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The Intelligent Robot Simulator is developed, a lightweight declarative simulator implemented as a Python library to support navigation learning and benchmarking through rapid construction of reusable and diverse scenarios and efficient execution on accessible platforms.
The Structured Subtask Chain (SSC), a state-transition representation that bridges these extremes, is proposed and instantiate the pipeline on BEHAVIOR-1K for logic verification and content completion.
The proposed DR-LfD (Decomposed and Reorganized Skills Learned from Demonstrations) framework is a framework that seamlessly integrates visuomotor policies into a TAMP-gated decision-making system, enabling reorganization of skills learned from different sources.
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