Zero-shot Imitation Policy via Search in Demonstration Dataset

Understand

Behavioral cloning uses a dataset of demonstrations to learn a policy.

• To overcome computationally expensive training procedures and address the policy adaptation problem, we propose to use latent spaces of pre-trained foundation models to index a demonstration dataset, instantly access similar relevant experiences, and copy behavior from these situations.
• Actions from a selected similar situation can be performed by the agent until representations of the agent's current situation and the selected experience diverge in the latent space.
• Thus, we formulate our control problem as a dynamic search problem over a dataset of experts' demonstrations.