Steeven Janny

Robot policies are predominantly learned with classical parametric variants of imitation learning or RL, where training stores the agent's behavior exclusively in the policy's network parameters, putting a heavy burden on the representation learning algorithm. We propose a new navigation agent equipped with non-parametric memory which explicitly indexes prior steps leading to critical events.