Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards
Summary: arXiv:2604.10110v1 Announce Type: new
Abstract
Large Language Models (LLMs) have become a key foundation for enabling personalized smart home experiences. While existing studies have explored how smart home assistants understand user queries to control devices in real time, their ability to perform memory-driven device control remains challenging from both evaluation and methodological perspectives.
Challenges in Memory-Driven Device Control
In terms of evaluation, existing benchmarks either focus on immediate device control or general open-domain memory retrieval tasks, and therefore cannot effectively evaluate a model’s ability to perform memory-driven device control. Methodologically, while memory-driven device control can be approached using Reinforcement Learning (RL), conventional RL methods generally rely on outcome-based supervision (i.e., whether the final task is achieved). This lack of intermediate feedback can lead to sub-optimal performance or local failures in fine-grained memory management tasks.
Introducing MemHomeLife
To address these issues, we first release MemHomeLife, built from anonymized real-world long-term user interaction logs. This dataset will allow researchers to explore the nuances of memory-driven interactions within smart homes and evaluate the performance of various models under realistic conditions.
MemHome: A Revolutionary Benchmark
To enable more fine-grained evaluation of different memory-related subtasks, we further construct MemHome, the first benchmark designed to systematically evaluate memory-driven device control in smart home scenarios. This benchmark aims to cover a wide range of tasks, including:
- Adding new devices to the home network
- Updating device settings based on user preferences
- Deleting devices that are no longer in use
- Utilizing memory for efficient task execution
The Importance of Multi-dimensional Rewards
One of the key innovations of this approach is the introduction of multi-dimensional rewards in the RL framework. By incorporating diverse reward signals, models can learn not only to achieve final outcomes but also to understand the intermediate steps necessary for effective memory management. This framework aims to improve the model’s ability to:
- Optimize memory usage
- Enhance user satisfaction by providing personalized responses
- Reduce errors in device control
Future Implications
The release of MemHomeLife and MemHome marks a significant step forward in the field of smart home technology. By providing a robust framework for evaluating memory-driven device control, researchers and developers can work towards creating more intelligent and responsive smart home systems. This advancement not only aims to enhance user experience but also to pave the way for innovative applications in the realm of artificial intelligence and machine learning.
Conclusion
As smart home technologies continue to evolve, the integration of advanced reinforcement learning techniques and comprehensive benchmarks like MemHome will play a crucial role in shaping the future of personalized home automation. The focus on memory-driven interactions will ultimately lead to smarter and more efficient living environments.