arXiv:2606.01230v1 Announce Type: new Abstract: Large language model agents are moving beyond text-only interaction toward physical-world control, with smart homes as a representative domain. Real domestic interaction requires understanding ambiguous intents, operating in dynamic environments, and performing multi-turn reasoning. However, existing methods struggle to generate high-quality training data for smart home agents. We propose HomeFlow, a verifiable data flywheel for this domain. HomeFlow uses HomeEnv as a unified simulation environment and HomeMaker to procedurally generate diverse h
The increasing sophistication of large language models is pushing their application beyond text, making physical-world control a natural next frontier for AI agents, especially in complex environments like smart homes.
This development addresses a critical bottleneck in training data generation for AI agents operating in real-world physical domains, paving the way for more robust and autonomous smart home systems.
The ability to generate high-quality, verifiable training data through simulated environments significantly accelerates the development and deployment of capable physical-world AI agents.
- · AI agent developers
- · Smart home device manufacturers
- · Simulation platform providers
- · Consumers of smart home technology
- · Companies reliant on manual data collection for robotics
- · Less adaptable smart home platforms
More capable and reliable smart home AI agents become widely available, enhancing home automation and personalized environments.
The simulated training paradigm extends to other physical domains, accelerating AI development in robotics and industrial automation.
The proliferation of highly autonomous AI agents in shared physical spaces necessitates new regulatory frameworks and safety standards for human-AI interaction.
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Read at arXiv cs.AI