Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments

Understand

The growing integration of robots in shared environments-such as warehouses, shopping centres, and hospitals-demands a deep understanding of the underlying dynamics and human behaviours, including how, when, and where individuals engage in various activities and interactions.

• This knowledge goes beyond simple correlation studies and requires a more comprehensive causal analysis.
• By leveraging causal inference to model cause-and-effect relationships, we can better anticipate critical environmental factors and enable autonomous robots to plan and execute tasks more effectively.
• To this end, we propose a novel causality-based decision-making framework that reasons over a learned causal model to assist the robot in deciding when and how to complete a given task.