- Ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering.
- In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning.
- Task examples:
- to stack all blocks on a table into a single tower
- to place all blocks on a table into two-block towers
- A neural net is trained
- such that
- when it takes as input
- the first demonstration demonstration and
- a state sampled from the second demonstration,
- it should predict the action corresponding to the sampled state.
- Our experiments show that the use of soft attention allows the model to generalize to conditions and tasks unseen in the training data