让机器人用大模型解释行为差异并协同修正认知偏差。
Model Reconciliation through Explainability and Collaborative Recovery in Assistive Robotics
- 用大模型分析人机认知差异，无需预设用户心理模型。
- 实验验证在轮椅机械臂系统中有效降低认知分歧。
- 适合人机协作、智能辅助机器人等场景研究者参考。
在人机协作场景中，尤其在共享控制应用中，确保机器人行为可解释至关重要。用户与机器人需对环境对象及其可操作动作保持一致的认知模型。本文提出一种模型协调框架，利用大语言模型预测并解释机器人与人类之间心理模型的差异，无需预先构建用户的心理模型。此外，该框架通过允许用户纠正机器人模型，解决解释后的模型分歧问题。我们在助行机器人领域实现该框架，使用真实轮椅式移动机械臂及其数字孪生体进行多组实验，验证了其有效性。
原文摘要 · Abstract (English)
Whenever humans and robots work together, it is essential that unexpected robot behavior can be explained to the user. Especially in applications such as shared control the user and the robot must share the same model of the objects in the world, and the actions that can be performed on these objects. In this paper, we achieve this with a so-called model reconciliation framework. We leverage a Large Language Model to predict and explain the difference between the robot's and the human's mental models, without the need of a formal mental model of the user. Furthermore, our framework aims to solve the model divergence after the explanation by allowing the human to correct the robot. We provide an implementation in an assistive robotics domain, where we conduct a set of experiments with a real wheelchair-based mobile manipulator and its digital twin.
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