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[2602.19930] Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learning
Summary
The paper discusses the limitations of current imitation learning systems, proposing a shift from mere memorization to fostering lifelong adaptability through compositional generalization.
Why It Matters
This research is significant as it addresses the foundational issues in imitation learning, suggesting a new paradigm that emphasizes adaptability. By redefining success metrics and exploring interdisciplinary approaches, it opens pathways for developing AI agents capable of thriving in dynamic environments, which is crucial for advancing artificial intelligence applications.
Key Takeaways
- Current imitation learning systems are primarily memorization-based.
- The paper advocates for compositional adaptability as a new objective.
- It proposes hybrid architectures and metrics for evaluating adaptability.
- Interdisciplinary research directions are suggested, drawing from cognitive science.
- Agents with adaptability can better operate in unpredictable environments.
Computer Science > Artificial Intelligence arXiv:2602.19930 (cs) [Submitted on 23 Feb 2026] Title:Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learning Authors:Nathan Gavenski, Felipe Meneguzzi, Odinaldo Rodrigues View a PDF of the paper titled Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learning, by Nathan Gavenski and 1 other authors View PDF HTML (experimental) Abstract:Imitation learning stands at a crossroads: despite decades of progress, current imitation learning agents remain sophisticated memorisation machines, excelling at replay but failing when contexts shift or goals evolve. This paper argues that this failure is not technical but foundational: imitation learning has been optimised for the wrong objective. We propose a research agenda that redefines success from perfect replay to compositional adaptability. Such adaptability hinges on learning behavioural primitives once and recombining them through novel contexts without retraining. We establish metrics for compositional generalisation, propose hybrid architectures, and outline interdisciplinary research directions drawing on cognitive science and cultural evolution. Agents that embed adaptability at the core of imitation learning thus have an essential capability for operating in an open-ended world. Comments: Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2602.19930 [cs.AI] (or arXiv:2602.19930v1 [cs.AI] for this version) https://doi.org/10....
