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Frontier stage
Keynote
LeadersGoldDiscovery

Transparent robot decision-making through interpretable and explainable AI

    • Date
    10 July 2026
    Timeframe
    12:00 - 12:15 CEST
    Duration
    15 minutes
    • Days
      Hours
      Min
      Sec

    Transparent decision-making enables humans to understand, interpret, and predict what robots do. Interpretable and explainable methods enhance transparency: interpretable methods clarify how a learned model reaches decisions, while explainable methods articulate why specific decisions were made. In this talk, I will first introduce our interpretable AI methods that generate compact, generalizable semantic models to infer human activities, enabling robots to gain a high-level understanding of human movement. Next, I will present our causal approach, which enables robots to anticipate and prevent both imminent and future failures, helping them understand why failures occur, learn from mistakes, and improve future performance. Finally, I will discuss how we combine these methods into a single framework that integrates symbolic planning with hierarchical reinforcement learning. This integration allows us to learn flexible, reusable robot policies for manipulation tasks, yielding coherent action sequences that are individually executable. Together, interpretable and explainable AI form the foundation for general-purpose robots capable of making complex decisions in dynamic and unpredictable environments, and for fostering the mutual understanding that effective human-robot collaboration requires.

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