Learning from prior experience is essential for autonomous robots, but directly reusing historical actions is often insufficient when the environment changes and previously successful behaviors are no longer applicable. This paper proposes an experience-to-thought learning framework that learns the underlying thinking activities behind historical robot materials rather than only their surface behaviors. An LLM analyzes accumulated experiences to extract diverse thoughts, including prediction, calculation, comparison, risk evaluation, causal analysis, reflection, planning, and verification. A model-agnostic temporal thought-learning mechanism then associates these thoughts with the evolving states and contextual conditions under which they are useful, allowing the same thought to guide different coordinated action sequences in different situations. The framework further supports open-ended growth of the thinking repertoire by detecting when existing thinking is insufficient, discovering candidate thoughts, and consolidating them into reusable new thinking activities. Experiments show that temporal thought learning substantially improves thought selection under temporally ambiguous situations and transfers more effectively than direct action-plan learning when the required behavior changes. The framework also successfully discovers, consolidates, and reuses a new thinking activity in previously unseen situations.