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Vision-language-action (VLA) models have achieved strong performance in embodied manipulation, but still lack a clear mechanism to balance behavioral stability with task-semantic sensitivity. We identify two complementary failure modes. Under task-preserving changes, where task semantics remain unchanged but scene appe...
Embodied agents replan frequently to recover from execution drift, partial observability, and coordination hazards, but each LLM-based replanning call can consume an accumulated textual context that grows over time and across agents. Once this context becomes large, replanning latency develops heavy tails and can miss...
Constructing simulation scenarios manually is time-consuming and often depends on platform-specific modeling experience. Existing large-language-model (LLM) methods are promising for interpreting operational documents, but they still struggle with long-document parsing, incomplete platform interfaces, auditable task ex...
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