Multi-Robot Collaboration for Trustworthy Lifelong Learning

机器人需要终身学习来获取、提炼和翻译新经验中的知识。然而，通过基于连续获取的信息做出决策，而不是从先验可用的大规模数据集中学习，终身学习对确保可信的人工智能提出了重大挑战：通过不够多样化的经验不断获取的同质数据可能导致有偏见的决策（例如，具有限于家庭的经验的机器人在暴露于，例如，公共空间）。为了确保机器人的推理是基于来自异质经验的证据，分布式学习，例如，联合学习允许多个机器人暴露于不同的场景，以协作共享他们的个人知识。虽然分布式学习使机器人能够“跳出框框”思考，但机器人的同质子组在异质队列的融合经验中注入系统性偏见的风险仍然存在。 该项目将分析分布式学习中的偏见来源，并将开发新的算法，以减轻多个机器人在聚集经验过程中的偏见，同时保持公平性并确保数据隐私。

Robots require lifelong learning to acquire, refine, and translate knowledge from new experiences. However, by making decisions based on information that is acquired continuously, as opposed to learning from large-scale datasets that are available a priori, lifelong learning raises significant challenges for the assurance of trustworthy AI: Homogeneous data acquired continually through insufficiently diverse experiences can lead to biased decisions (e.g., a robot with experience limited to households cannot straightforwardly translate its prior knowledge when exposed to, e.g., public spaces). To ensure that a robot's reasoning is based on evidence from heterogenous experiences, distributed learning, e.g., federated learning, allows multiple robots expose to diverse scenarios to collaboratively share their individual knowledge. While distributed learning enables robots to think 'outside of the box', a risk remains that homogenous subgroups of robots instil systematic bias in the fused experience of the heterogenous cohort. This project will analyse the source of bias in distributed learning, and will develop novel algorithms that mitigate bias during the aggregation of experiences from multiple robots, while preserving fairness and ensuring data privacy.