Few-Shot Imitation Learning

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Most implemented papers archive 2025-07-28

8 shown of 8 papers with code (15 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
  23 Jun 2023 4 repositories listed
  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
  8 Oct 2018 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  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.
• Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
  10 Dec 2024 1 repository listed Syntology ran 0 of 6 samples · 6 unverified · 6 pointer-only (licence)
  In this paper, we introduce a few-shot behavior cloning framework to simultaneously generalize to unseen embodiments and tasks using a few (\emph{e.
• ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AI
  3 Oct 2024 1 repository listed Syntology ran 3 of 7 samples · 4 unverified · 7 pointer-only (licence)
  Intelligent embodied agents need to quickly adapt to new scenarios by integrating long histories of experience into decision-making.
• FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning
  29 Aug 2024 1 repository listed Syntology ran 2 of 4 samples · 2 unverified
  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…
• PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control
  16 Feb 2024 1 repository listed Syntology ran 8 of 11 samples · 3 unverified
  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…
• Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
  9 Feb 2024 1 repository listed Syntology ran 6 of 8 samples · 2 unverified · 8 pointer-only (licence)
  We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.
• Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
  14 Oct 2022 1 repository listed
  In the abstract environment, complex dynamics such as physical manipulation are removed, making abstract trajectories easier to generate.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.