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Few-Shot Imitation Learning
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Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
Transfering Hierarchical Structure with Dual Meta Imitation Learning
Stage Conscious Attention Network (SCAN) : A Demonstration-Conditioned Policy for Few-Shot Imitation
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
Task-Embedded Control Networks for Few-Shot Imitation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.