Human activity recognition (HAR) is usually framed as gradient-based training of neural networks.
Agentic Heuristic Learning (AHL) Studio explores a complementary view inspired by human cognitive learning: people learn activities by remembering examples, forming rules, and repairing mistakes, not by backpropagating.
This proposed tool implements AHL for HAR: a learning-time agent reasons over sensor protocols, proposes executable heuristic policies, records repair traces, and exports an LLM-free policy for edge deployment.
We focus on the HAR benchmark family and provide an end-to-end workflow from dataset observation to edge-oriented export.
On eleven HAR datasets evaluated so far, AHL policies reach strong executable-policy performance while remaining inspectable, editable, and replayable
[footnote: https://github.com/zhaxidele/ahl-ts-studio]