Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learning

2026-02-23

AI Summary

This work addresses the limited generalization of existing imitation learning methods, which often merely memorize and replay demonstrated behaviors and struggle to adapt when environments or goals change. To overcome this, the paper proposes a novel paradigm centered on compositional adaptation: agents learn reusable behavioral primitives in a single exposure and dynamically recombine them in novel contexts to achieve lifelong adaptability. Drawing inspiration from cognitive science and cultural evolution theory, the approach employs a hybrid architecture that integrates behavioral primitive learning with mechanisms for compositional generalization. Furthermore, the study introduces the first evaluation benchmark specifically designed for assessing compositional generalization, establishing a foundation for developing agents capable of continuous adaptation in open-world settings.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Multiagent Systems: Adversarial Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• imitation learning
• lifelong adaptability
• compositional generalisation
• behavioral primitives
• context shift

Innovation

• compositional adaptability
• behavioral primitives
• imitation learning
• lifelong learning
• compositional generalization