Value from Observations: Towards Large-Scale Imitation Learning via Self-Improvement

2025-07-09

AI Summary

Existing imitation from observation (IfO) approaches typically assume bimodal data distributions, rendering them ill-suited for real-world scenarios characterized by complex, hierarchical data structures. Method: We propose an action-label-free, iterative self-improving IfO framework that introduces a value-function-based information transfer mechanism to enable cross-level knowledge distillation over mixed expert and non-expert demonstrations. Integrating reinforcement learning with contrastive learning, our method establishes an end-to-end unsupervised behavioral modeling pipeline. Contribution/Results: To the best of our knowledge, this is the first IfO approach achieving fully action-label-free imitation under hierarchical data distributions. It significantly enhances robustness and scalability. Empirical evaluations demonstrate strong adaptability to data distribution shifts and uncover fundamental performance bottlenecks of conventional methods under non-ideal, realistic data conditions.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction

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: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Imitation Learning from Observation (IfO) offers a powerful way to learn behaviors at large-scale: Unlike behavior cloning or offline reinforcement learning, IfO can leverage action-free demonstrations and thus circumvents the need for costly action-labeled demonstrations or reward functions. However, current IfO research focuses on idealized scenarios with mostly bimodal-quality data distributions, restricting the meaningfulness of the results. In contrast, this paper investigates more nuanced distributions and introduces a method to learn from such data, moving closer to a paradigm in which imitation learning can be performed iteratively via self-improvement. Our method adapts RL-based imitation learning to action-free demonstrations, using a value function to transfer information between expert and non-expert data. Through comprehensive evaluation, we delineate the relation between different data distributions and the applicability of algorithms and highlight the limitations of established methods. Our findings provide valuable insights for developing more robust and practical IfO techniques on a path to scalable behaviour learning.

Problem

• Enables imitation learning without action-labeled demonstrations
• Addresses limitations of current IfO with nuanced data distributions
• Proposes self-improvement method using value functions for IfO

Innovation

• Uses value function for action-free imitation learning
• Adapts RL-based learning to nuanced data distributions
• Enables iterative self-improvement in imitation learning