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You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition

Year: 2026

Type: preprint

Abstract: 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}.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.16065

Authors: Yuan, Siyu, Zhang, He, Bian, Sizhen, Guo, Bin

Institutions: University of Kaiserslautern, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Northwestern Polytechnical University

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Fundamental Research Funds for the Central Universities

OpenAlex ID: W7213437236

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