Behavioral Cloning Mystery

2026-10-05

AI Summary

This study addresses the bottleneck that counterintuitive phenomena in behavior cloning (BC) are difficult to investigate controllably within real or simulated environments by proposing OCBench. This benchmark integrates GPU-accelerated simulation with scripted policy generation to construct a controllable experimental platform that combines high computational efficiency with human-like demonstration characteristics, precisely modeling human teaching data distributions. Leveraging this platform, the work successfully reproduces multiple BC anomalies and rigorously verifies and refutes existing theoretical hypotheses through controlled experiments. This research fills the gap in empirical analysis of anomalous BC mechanisms, providing a scientific and reproducible paradigm for understanding failure modes in imitation learning.

Abstract

Behavioral cloning (BC), despite its simplicity, exhibits many counterintuitive phenomena in the real world. For example, the performance of BC often keeps increasing as the model overfits more to the dataset, and fully closed-loop policies often completely fail without action chunking. Unfortunately, properly studying these anecdotal phenomena ("behavioral cloning mysteries") is challenging: in the real world, datasets and experiments are costly and not fully controllable; in simulation with synthetic data, these phenomena are often not easily observed partly due to the discrepancy between scripted policies and human demonstrations. In this work, we propose OCBench, a robotic manipulation benchmark with controllable scripted policies that have similar properties to human demonstrations. We show that, by mimicking key properties of human demonstrations, OCBench reproduces many anecdotal BC-related phenomena in controlled settings. With its GPU-accelerated environments and scripted policies, we demonstrate how OCBench enables scientific studies of previously reported BC-related phenomena by analyzing and refuting various hypotheses. Project page: https://seohong.me/projects/ocbench

Problem

• Behavioral Cloning
• Robotic Manipulation
• Benchmark
• Simulation-to-Real Gap
• Policy Learning

Innovation

• Behavioral Cloning
• OCBench
• Robotic Manipulation
• Scripted Policies
• GPU-accelerated Environments