TECoSA Research Seminar: Toward Explainable Robots: Integrating Modeling Humans and Adaptive Learning

[For TECoSA members only.]

Speaker: Elmira Yadollahi, TECoSA postdoc
(Venue, Zoom link and sign-up link circulated to members)
Please email vickid@kth.se if you have any questions.

ABSTRACT: Explainability in human‑robot collaboration refers to the robot’s ability to provide clear, transparent, and interpretable explanations for its actions and decisions. It bridges the communication gap between complex machine functionalities and humans. An active area of investigation in robotics and AI is understanding and generating explanations that can enhance collaboration and mutual understanding between humans and machines. A key to achieving such seamless collaborations is understanding end-users, whether naive or expert, and tailoring explanation features that are intuitive, user-centered, and contextually relevant. Advancing on the topic includes modelling humans’ expectations for generating explanations and developing metrics to evaluate them and assess how effectively autonomous systems communicate their intentions, actions, and decision-making rationale. This seminar will present multiple user studies, focusing on understanding how humans perceive robots’ or autonomous vehicles’ explanations in different scenarios, e.g., failures or accidents. Subsequently, we transition to a phase of basic modelling of human expectations and machine understanding, leading us to develop adaptive learning approaches that leverage reinforcement learning strategies to refine and enhance these machines’ explanatory capabilities.