Personalizing Large Language Model Planners for Household Robotics via Reinforced Self-Training

Core Concepts

LLM-Personalize, a framework that personalizes large language model (LLM) planners for household robotics tasks by combining imitation learning and iterative self-training, achieves significant improvements in alignment with user preferences compared to existing LLM-based planners.

Abstract

The paper introduces LLM-Personalize, a framework for personalizing large language model (LLM) planners for household robotics tasks. The key components of the framework are: Context Generator: Maintains and updates an internal representation of the household state, including rooms, receptacles, and objects, based on the robot's local observations. This information is provided as a prompt to the LLM planner. LLM Planner: An LLM-based module that generates high-level plans as a sequence of actions (e.g., go to object, pick up object, place object on receptacle) in an iterative manner to handle partial observability. Optimization Pipeline: Combines imitation learning and iterative self-training to personalize the LLM planner to user preferences. Imitation Learning: Bootstraps the LLM planner to effectively interpret complex input contexts, produce executable plans, and perform initial alignment with example user preferences. Iterative Self-Training: Allows the LLM planner to further explore and refine its planning strategies based on user preferences collected through interactions. The authors evaluate LLM-Personalize on the Housekeep benchmark, a challenging simulated real-world 3D environment for household rearrangement tasks. The results show that LLM-Personalize achieves more than a 30% increase in success rate over existing LLM-based planners, demonstrating significantly improved alignment with human preferences. The authors also conduct ablation studies to analyze the improvements in plan executability, exploration vs. exploitation behavior, and cross-domain generalization of LLM-Personalize.

Stats

"LLM-Personalize achieves more than a 30 percent increase in success rate over existing LLM planners, showcasing significantly improved alignment with human preferences."

Quotes

"Central to our approach is the optimization pipeline, which combines imitation learning and iterative self-training to personalize the LLM planner." "We show in our experiments that LLM-Personalize outperforms state-of-the-art baseline LLM-based planners with more than a 30 percent increase in success rate, as a result of improved understanding and alignment with human preferences."

Key Insights Distilled From

LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots

by Dongge Han,T... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14285.pdf

Deeper Inquiries

How can the LLM-Personalize framework be extended to handle more complex household tasks beyond object rearrangement, such as multi-step procedures or interactions with household appliances?

The LLM-Personalize framework can be extended to handle more complex household tasks by incorporating hierarchical planning and task decomposition. For multi-step procedures, the framework can be enhanced to generate sequences of high-level actions that break down the task into smaller subtasks. This hierarchical planning approach allows the LLM planner to navigate through a series of steps to accomplish more intricate tasks efficiently. Additionally, incorporating context-aware reasoning mechanisms can enable the model to adapt its plans based on the changing environment and user preferences. When it comes to interactions with household appliances, the framework can be augmented with domain-specific knowledge about the functionalities and operations of various appliances. By integrating this knowledge into the context generator and LLM planner, the agent can generate plans that involve interacting with appliances in a meaningful way. For instance, the model can be trained to understand commands related to operating specific appliances, such as turning on a coffee machine or adjusting the temperature of an oven. This would require additional training data and fine-tuning to align the LLM planner with the intricacies of appliance interactions.

How can the personalization of LLM planners be further improved by incorporating additional modalities of user feedback, such as natural language instructions or demonstrations?

Incorporating additional modalities of user feedback, such as natural language instructions or demonstrations, can significantly enhance the personalization of LLM planners in household robotics tasks. One approach is to integrate a multimodal learning framework that combines text inputs with visual or auditory cues. By processing natural language instructions alongside visual demonstrations or audio commands, the LLM planner can gain a more comprehensive understanding of user preferences and task requirements. Natural language instructions can provide explicit guidance on user preferences and task objectives, allowing the LLM planner to align its plans more closely with user expectations. By training the model on a diverse dataset of natural language instructions, the LLM can learn to interpret and execute a wide range of user commands accurately. Visual demonstrations, on the other hand, offer a practical way to showcase desired behaviors or task executions. By incorporating video or image data alongside textual prompts, the LLM planner can learn from visual cues and improve its ability to generate plans that match the demonstrated actions. This can be particularly useful for tasks that involve intricate movements or interactions with physical objects. Overall, by integrating multiple modalities of user feedback, the LLM planner can enhance its personalization capabilities and adapt more effectively to individual user preferences and task contexts in household robotics applications.

What are the potential challenges and limitations in scaling the self-training approach to a broader range of user preferences and household environments?

Scaling the self-training approach to accommodate a broader range of user preferences and household environments may pose several challenges and limitations: Data Diversity: One challenge is ensuring that the training data used for self-training is diverse and representative of the various preferences and scenarios encountered in different households. Limited or biased training data can lead to suboptimal personalization and generalization. Curriculum Design: Designing an effective curriculum for self-training that gradually exposes the model to increasingly complex tasks and preferences can be challenging. Balancing the exploration of new preferences with the exploitation of learned behaviors is crucial for robust performance. Sample Efficiency: Self-training methods often require a large amount of interaction data to fine-tune the model effectively. Scaling to a broader range of preferences and environments may necessitate more extensive data collection, which can be resource-intensive. Overfitting: As the model undergoes multiple iterations of self-training, there is a risk of overfitting to the training data and specific user preferences. Regularization techniques and careful monitoring of model performance are essential to prevent overfitting. Transfer Learning: Adapting a self-trained model to new user preferences or household environments may require additional fine-tuning or transfer learning strategies. Ensuring the model can generalize well to unseen scenarios is crucial for real-world deployment. Interpretability: As the model becomes more personalized through self-training, maintaining interpretability and transparency in its decision-making process can become challenging. Understanding how the model incorporates user feedback and preferences is essential for trust and usability. Addressing these challenges and limitations requires a thoughtful approach to data collection, model design, and training procedures to ensure the scalability and effectiveness of the self-training approach in diverse household robotics settings.