Self-Supervised Keyframe Discovery for Horizon-Invariant Behavior Cloning
Keyframe Mnemonics (KM) addresses Behavior Cloning challenges in non-Markovian environments by decomposing the problem into finding critical observations and training a policy conditioned on them. It uses self-supervised learning to discover sparse, information-rich 'keyframes' from expert data, leading to a policy that maintains performance across long time horizons.