ACORN: Adaptive Contrastive Optimization for Safe and Robust Fine-Grained Robotic Manipulation

TL;DR

ACORN is a novel contrastive learning algorithm that improves the safety and robustness of robotic manipulation policies by aligning with expert demonstrations and diverging from unsafe behaviors, especially under environmental disturbances.

Contribution

The paper introduces ACORN, a plug-and-play contrastive optimization method that enhances policy robustness and safety in embodied AI manipulation tasks without sacrificing performance.

Findings

ACORN improves safety metrics by up to 23% under disturbances.

ACORN effectively balances trajectory alignment with expert demonstrations and divergence from unsafe behaviors.

The method maintains computational efficiency through structured Gaussian noise injection.

Abstract

Embodied AI research has traditionally emphasized performance metrics such as success rate and cumulative reward, overlooking critical robustness and safety considerations that emerge during real-world deployment. In actual environments, agents continuously encounter unpredicted situations and distribution shifts, causing seemingly reliable policies to experience catastrophic failures, particularly in manipulation tasks. To address this gap, we introduce four novel safety-centric metrics that quantify an agent's resilience to environmental perturbations. Building on these metrics, we present Adaptive Contrastive Optimization for Robust Manipulation (ACORN), a plug-and-play algorithm that enhances policy robustness without sacrificing performance. ACORN leverages contrastive learning to simultaneously align trajectories with expert demonstrations while diverging from potentially unsafe behaviors. Our approach efficiently generates informative negative samples through structured Gaussian noise injection, employing a double perturbation technique that maintains sample diversity while minimizing computational overhead. Comprehensive experiments across diverse manipulation environments validate ACORN's effectiveness, yielding improvements of up to 23% in safety metrics under disturbance compared to baseline methods. These findings underscore ACORN's significant potential for enabling reliable deployment of embodied agents in safety-critical real-world applications.