SAFARI: Safe and Active Robot Imitation Learning with Imagination

Understand

One of the main issues in Imitation Learning is the erroneous behavior of an agent when facing out-of-distribution situations, not covered by the set of demonstrations given by the expert.

• In this work, we tackle this problem by introducing a novel active learning and control algorithm, SAFARI.
• During training, it allows an agent to request further human demonstrations when these out-of-distribution situations are met.
• At deployment, it combines model-free acting using behavioural cloning with model-based planning to reduce state-distribution shift, using future state reconstruction as a test for state familiarity.