Thinking-Triggered Aspect Learning for Autonomous Knowledge Acquisition and Transfer in Robots

Autonomous robots require not only reliable task execution but also the ability to identify what new local knowledge should be learned, when explicit thinking is necessary, and when learned knowledge can be reused automatically. However, existing methods often bind learned knowledge to predefined tasks or fixed reasoning procedures, making it difficult to autonomously discover reusable local concerns and reduce repeated reasoning after adaptation. This paper proposes a thinking-triggered aspect learning framework for autonomous knowledge acquisition and transfer. Routine behavior is handled by automatic models, while novelty, uncertainty, or periodic heartbeat inspection triggers thinking to localize unresolved concerns. The robot then discovers task-independent aspects containing relevant factors, applicability conditions, handling principles, and local automatic models. Validated aspects are reused across tasks and domains, while aspect-specific calibration and checkpoint decay progressively convert explicit thinking into automatic processing; environmental changes can reactivate thinking and initiate new aspect acquisition. Experiments show aspect and factor F1 scores of 0.975 and 0.965, perfect zero-shot cross-task and cross-domain transfer in all successfully validated source runs, and a reduction in thinking rate from 0.508 to 0.100 after familiarization; the proposed method also reduces LLM calls by 76.4% compared with Always Think while achieving 0.976 post-validation detection and handling success.