Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning

Understand

We address one-shot imitation learning, where the goal is to execute a previously unseen task based on a single demonstration.

• While there has been exciting progress in this direction, most of the approaches still require a few hundred tasks for meta-training, which limits the scalability of the approaches.
• Our main contribution is to formulate one-shot imitation learning as a symbolic planning problem along with the symbol grounding problem.
• This formulation disentangles the policy execution from the inter-task generalization and leads to better data efficiency.