Reshaping Action Error Distributions for Reliable Vision-Language-Action Models

TL;DR

This paper introduces a novel training approach for vision-language-action models in robotics by reshaping action error distributions using Minimum Error Entropy, leading to improved success rates and robustness without extra inference costs.

Contribution

It proposes integrating Minimum Error Entropy into VLA training, moving beyond traditional MSE regression, and provides theoretical analysis of its effectiveness.

Findings

Consistent success rate improvements across benchmarks.

Enhanced robustness in noisy and few-shot settings.

Negligible additional training cost and no inference impact.

Abstract

In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies. Most existing VLA frameworks rely on standard supervised objectives, typically cross-entropy for discrete actions and mean squared error (MSE) for continuous action regression, which impose strong pointwise constraints on individual predictions. In this work, we focus on continuous-action VLA models and move beyond conventional MSE-based regression by reshaping action error distributions during training. Drawing on information-theoretic principles, we introduce Minimum Error Entropy (MEE) into modern VLA architectures and propose a trajectory-level MEE objective, together with two weighted variants, combined with MSE for continuous-action VLA training. We evaluate our approaches across standard, few-shot, and noisy settings on multiple representative VLA architectures, using simulation benchmarks such as LIBERO and SimplerEnv as well as real-world robotic manipulation tasks. Experimental results demonstrate consistent improvements in success rates and robustness across these settings. Under imbalanced data regimes, the gains persist within a well-characterized operating range, while incurring negligible additional training cost and no impact on inference efficiency. We further provide theoretical analyses that explain why MEE-based supervision is effective and characterize its practical range. Project Page: https://cognition2actionlab.github.io/VLA-TMEE.github.io/