Using Causal Models for Learning from Demonstration

Most learning from demonstration algorithms are implemented with a certain set of variables that are known to be important for the agent. The agent is hardcoded to use those variables for learning the task (or a set of parameters). In this work we try to understand the causal structure of a demonstrated task in order to find: which variables cause what other variables to change, and which variables are independent from the others. We used a realistic simulator to record a simple pick and place task demonstration data, and recovered different causal models using the data in Tetrad, a computer program that searches for causal and statistical models. Our findings show that it is possible to deduce irrelevant variables to a demonstrated task, using the recovered causal structure.