AHAT: Abstract Human Instructions and Scene Graphs for Long-Horizon Planning

AHAT Dataset is the official dataset for the paper 'Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks'. The dataset contains abstract human instructions, textual scene graphs, and plans used in the AHAT research. It was authored by SII-liyang2024 and last updated on Hugging Face in April 2026.

Use Cases

• Training AI agents for long-horizon planning based on abstract human instructions.
• Evaluating scene graph generation and understanding models based on textual scene graph data.
• Benchmarking planning algorithms for complex, multi-step tasks in simulated environments.

Strengths

• Dataset is the official resource for a published research paper, providing a direct link to academic work.
• Last update was recorded on 2026-04-23, indicating recent maintenance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license information are unknown, which may limit suitability assessment.
• Data may reflect bias inherent to its source and collection method for the specific research project.

Provenance

Source

SII-liyang2024 on Hugging Face.

Collection Method

Created for the research paper 'Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks'.

Time Range

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Freshness

Last updated 2026-04 23 16:39:09.

Geography

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