Causal Influence Aware Counterfactual Data Augmentation

Pre-recorded data and human demonstrations are practical resources for teaching robots complex behaviors.However, the combinatorial nature of real-world scenarios requires a huge amount of data to prevent neural network policies from picking up on spurious and non-causal factors.We propose CAIAC, a data augmentation method that creates synthetic samples from a fixed dataset without the need to perform new environment interactions.Motivated by the fact that an agent may only modify the environment through its actions, we swap causally action-unaffected parts of the state-space from different observed trajectories.In several environment benchmarks, we observe an increase in generalization capabilities and sample efficiency.