Few-Shot Imitation Learning
8 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Few-Shot Imitation Learning
Most implemented papers
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.
Task-Embedded Control Networks for Few-Shot Imitation Learning
stepjam/TecNets
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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.
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.
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.
PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
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.
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning
lihenglin/bridge_training_code
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We propose FlowRetrieval, an approach that leverages optical flow representations for both extracting similar motions to target tasks from prior data, and for guiding learning of a policy that can maximally benefit from such data.
ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI
aielawady/relic
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Intelligent embodied agents need to quickly adapt to new scenarios by integrating long histories of experience into decision-making.
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
seongwoongcho/meta-controller
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In this paper, we introduce a few-shot behavior cloning framework to simultaneously generalize to unseen embodiments and tasks using a few (\emph{e. g.,} five) reward-free demonstrations.
