Human Activity Recognition (HAR) is usually tackled by gradient‑based training of neural networks. Agentic Heuristic Learning (AHL) Studio adopts a complementary perspective inspired by human cognition: people learn activities by memorizing examples, forming rules, and fixing mistakes rather than by back‑propagation. The tool equips a learning‑time agent that reasons over sensor protocols, proposes executable heuristic policies, records repair traces, and exports a policy that does not rely on large language models for edge deployment. An end‑to‑end workflow is provided for the HAR benchmark family, covering dataset observation to edge‑oriented export. Evaluated on eleven HAR datasets, AHL policies achieve strong executable performance while remaining inspectable, editable, and replayable. https://github.com/zhaxidele/ahl-ts-studio
Review This work demonstrates that effective activity recognition can be achieved without conventional training, offering a transparent and edge‑friendly solution through heuristic learning.