Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent Collaboration

Konsep Inti

Agents can bootstrap large nonlinear models to learn the parameters of a low-capacity model, which can then be efficiently updated online using logistic regression to align with a person's preferences during collaborative tasks.

Abstrak

The paper proposes a method called BLR-HAC (Bootstrapped Logistic Regression for Human Agent Collaboration) that combines the strengths of pretrained large, nonlinear models and low-capacity models trained online via logistic regression to enable efficient learning in human-robot collaborations.

The key insights are:

1. Agents assisting people need to have well-initialized policies that can adapt quickly to align with their partners' reward functions. Initializing policies to maximize performance with unknown partners can be achieved by bootstrapping nonlinear models using imitation learning over large, offline datasets. However, these large models require prohibitive computation to fine-tune in-situ.
2. In contrast, online logistic regression using low-capacity models performs rapid inference and fine-tuning updates, allowing effective use of immediate in-task behavior for reward function alignment. However, these low-capacity models cannot be bootstrapped as effectively by offline datasets and thus have poor initializations.
3. BLR-HAC addresses this by first pretraining a large nonlinear model to learn the parameters of a low-capacity model, which is then updated online using logistic regression. This allows the agent to benefit from both good zero-shot performance and fast online adaptation.

The paper evaluates BLR-HAC in a simulated surface rearrangement task, where an agent assists a person in rearranging objects. The results show that BLR-HAC outperforms baseline low-capacity models and large, nonlinear models trained with behavior cloning in zero-shot coordination. It also achieves similar performance to a fine-tuned transformer model but requires a fraction of the compute.

Statistik

The paper does not provide any specific numerical data or statistics. It focuses on describing the proposed algorithm and evaluating its performance through simulated experiments.

Kutipan

The paper does not contain any direct quotes that are particularly striking or support the key arguments.

Wawasan Utama Disaring Dari

Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent Collaboration

by Benjamin A N... pada arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10733.pdf

Pertanyaan yang Lebih Dalam

How would the performance of BLR-HAC be affected by the size and diversity of the offline dataset used for pretraining the large nonlinear model

The performance of BLR-HAC would be significantly impacted by the size and diversity of the offline dataset used for pretraining the large nonlinear model. A larger and more diverse dataset would provide a richer representation of user preferences, allowing the large nonlinear model to capture a wider range of behaviors and preferences. This would result in a more robust initialization for the low-capacity model used in the online adaptation phase. With a larger dataset, the large nonlinear model would have a better understanding of the various nuances and complexities of user preferences, leading to improved zero-shot performance for the assistive agent. On the other hand, a smaller or less diverse dataset may limit the ability of the large nonlinear model to capture the full spectrum of user preferences, potentially leading to suboptimal performance during online adaptation.

What are the potential limitations or failure modes of the BLR-HAC approach when dealing with highly complex or rapidly changing user preferences

While BLR-HAC offers a promising approach to aligning assistive agents with user preferences, there are potential limitations and failure modes to consider, especially when dealing with highly complex or rapidly changing user preferences. One limitation could be the model's ability to generalize to out-of-distribution preferences not adequately represented in the offline dataset. If the dataset does not encompass a wide range of user behaviors and preferences, the model may struggle to adapt effectively to novel or unexpected user preferences during online collaboration. Additionally, rapidly changing user preferences could pose a challenge for the model's adaptation speed. If user preferences shift frequently or unpredictably, the model may require more frequent updates and adjustments to accurately align with the user's evolving preferences. This could lead to increased computational overhead and potentially slower adaptation rates, impacting the overall performance of the assistive agent.

Could the ideas behind BLR-HAC be extended to other types of human-agent collaboration tasks beyond household rearrangement, such as task planning or navigation

The concepts and principles behind BLR-HAC could indeed be extended to other types of human-agent collaboration tasks beyond household rearrangement. Tasks such as task planning or navigation could benefit from a similar approach that combines the strengths of pretraining large nonlinear models with fast online adaptation using low-capacity models. For task planning, the model could be pretrained on a diverse set of task demonstrations to learn general task structures and preferences, then adapt online to specific user preferences and goals during task execution. In navigation tasks, the model could leverage offline datasets of different navigation scenarios to initialize the agent's policies, then adapt in real-time to user preferences and environmental changes. By applying the BLR-HAC framework to various human-agent collaboration tasks, it is possible to create adaptive and personalized assistive agents that can efficiently align with user preferences in dynamic and complex environments.