one-shot-learning

OpenAI proposes a meta-learning framework for one-shot imitation learning that enables robots to learn new tasks from a single demonstration and generalize to new instances without task-specific engineering. The approach uses soft attention mechanisms to allow neural networks trained on diverse task pairs to perform well on unseen tasks at test time.