Few-Shot Imitation Learning
5 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers with no code
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets
Concretely, we propose a simple approach that uses a small amount of downstream expert data to selectively query relevant behaviors from an offline, unlabeled dataset (including many sub-optimal behaviors).
Transfering Hierarchical Structure with Dual Meta Imitation Learning
Hierarchical Imitation Learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations.
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation
(3) Learning from a different expert.
Transferring Hierarchical Structure with Dual Meta Imitation Learning
Hierarchical Imitation learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations.
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent's ability to query the environment for transitions and rewards is effectively unlimited.
