Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation

Understand

Robotic systems are ever more capable of automation and fulfilment of complex tasks, particularly with reliance on recent advances in intelligent systems, deep learning and artificial intelligence.

• However, as robots and humans come closer in their interactions, the matter of interpretability, or explainability of robot decision-making processes for the human grows in importance.
• A successful interaction and collaboration will only take place through mutual understanding of underlying representations of the environment and the task at hand.
• This is currently a challenge in deep learning systems.