Few-Shot Imitation Learning
5 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers
PRISE: Learning Temporal Action Abstractions as a Sequence Compression Problem
To do so, we bring a subtle but critical component of LLM training pipelines -- input tokenization via byte pair encoding (BPE) -- to the seemingly distant task of learning skills of variable time span in continuous control domains.
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
Despite its simplicity this baseline is competitive with meta-learning methods on a variety of conditions and is able to imitate target policies trained on unseen variations of the original environment.
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
In the abstract environment, complex dynamics such as physical manipulation are removed, making abstract trajectories easier to generate.
Task-Embedded Control Networks for Few-Shot Imitation Learning
Despite this, most robot learning approaches have focused on learning a single task, from scratch, with a limited notion of generalisation, and no way of leveraging the knowledge to learn other tasks more efficiently.
