Beyond Success: Refining Elegant Robot Manipulation from Mixed-Quality Data via Just-in-Time Intervention
Yanbo Mao, Jianlong Fu, Ruoxuan Zhang, Hongxia Xie, Meibao Yao

TL;DR
This paper introduces a framework for improving robotic manipulation execution quality by using an Elegance Critic and Just-in-Time Intervention, which refine actions based on implicit task constraints without retraining the base policy.
Contribution
It proposes a decoupled refinement framework with an Elegance Critic trained via offline Calibrated Q-Learning and a JITI mechanism for on-demand intervention, enhancing execution quality in VLA models.
Findings
Significant improvement in execution quality on LIBERO-Elegant benchmark.
Effective refinement on unseen manipulation tasks.
JITI mechanism reduces unnecessary interventions.
Abstract
Vision-Language-Action (VLA) models have enabled notable progress in general-purpose robotic manipulation, yet their learned policies often exhibit variable execution quality. We attribute this variability to the mixed-quality nature of human demonstrations, where the implicit principles that govern how actions should be carried out are only partially satisfied. To address this challenge, we introduce the LIBERO-Elegant benchmark with explicit criteria for evaluating execution quality. Using these criteria, we develop a decoupled refinement framework that improves execution quality without modifying or retraining the base VLA policy. We formalize Elegant Execution as the satisfaction of Implicit Task Constraints (ITCs) and train an Elegance Critic via offline Calibrated Q-Learning to estimate the expected quality of candidate actions. At inference time, a Just-in-Time Intervention…
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Taxonomy
TopicsRobot Manipulation and Learning · Multimodal Machine Learning Applications · Reinforcement Learning in Robotics
