When does predictive inverse dynamics outperform behavior cloning?

2026-01-29

AI Summary

This work addresses the performance degradation of behavioral cloning under limited expert demonstrations and investigates the superior sample efficiency of Predictive Inverse Dynamics Models (PIDMs), whose underlying mechanism has remained unclear. We theoretically analyze PIDMs through the bias–variance tradeoff, revealing that while incorporating future state prediction introduces bias, it substantially reduces the variance in inverse dynamics estimation, thereby enhancing sample efficiency. By integrating a state predictor with an inverse dynamics model and leveraging additional data for both theoretical analysis and empirical validation, we demonstrate that PIDMs achieve comparable performance to behavioral cloning using only one-third of the demonstration data in 2D navigation tasks. In high-dimensional 3D environments with visual inputs, PIDMs reduce the required demonstration data by over 66%.

Technology Category

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

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Behavior cloning (BC) is a practical offline imitation learning method, but it often fails when expert demonstrations are limited. Recent works have introduced a class of architectures named predictive inverse dynamics models (PIDM) that combine a future state predictor with an inverse dynamics model (IDM). While PIDM often outperforms BC, the reasons behind its benefits remain unclear. In this paper, we provide a theoretical explanation: PIDM introduces a bias-variance tradeoff. While predicting the future state introduces bias, conditioning the IDM on the prediction can significantly reduce variance. We establish conditions on the state predictor bias for PIDM to achieve lower prediction error and higher sample efficiency than BC, with the gap widening when additional data sources are available. We validate the theoretical insights empirically in 2D navigation tasks, where BC requires up to five times (three times on average) more demonstrations than PIDM to reach comparable performance; and in a complex 3D environment in a modern video game with high-dimensional visual inputs and stochastic transitions, where BC requires over 66\% more samples than PIDM.

Problem

• behavior cloning
• predictive inverse dynamics
• imitation learning
• bias-variance tradeoff
• sample efficiency

Innovation

• predictive inverse dynamics
• behavior cloning
• bias-variance tradeoff
• sample efficiency
• imitation learning