提出新评估方法，揭示视觉杂乱如何显著降低机器人操作成功率
Distracted Robot: How Visual Clutter Undermine Robotic Manipulation
- 从心理物理学角度构建统一杂乱度量，融合环境与干扰物特征
- 实测显示杂乱使操作成功率下降高达34%，不同模型表现差异大
- 杂乱度量可预测性能退化，但微调无法完全弥补杂乱负面影响
本文提出一种评估机器人在杂乱场景中操作策略性能的新协议。不同于以往研究，我们从心理物理学视角出发，采用统一的杂乱度量，综合考虑环境因素、干扰物数量、特征及排列方式。基于该度量，我们在高保真仿真和真实世界中系统构建评估场景，并对多种视觉-语言-动作（VLA）模型进行广泛实验。结果表明，场景杂乱对策略性能有显著影响，成功率最高下降34%；尽管平均表现相近，不同VLA模型存在独特脆弱性，成功场景共识率较低。进一步发现，该杂乱度量能有效预测性能退化，并分析了干扰物数量及其遮挡作用的影响。最后，虽然增强数据微调有一定效果，但无法同等缓解所有杂乱带来的性能损失。
原文摘要 · Abstract (English)
In this work, we propose an evaluation protocol for examining the performance of robotic manipulation policies in cluttered scenes. Contrary to prior works, we approach evaluation from a psychophysical perspective, therefore we use a unified measure of clutter that accounts for environmental factors as well as the distractors quantity, characteristics, and arrangement. Using this measure, we systematically construct evaluation scenarios in both hyper-realistic simulation and real-world and conduct extensive experimentation on manipulation policies, in particular vision-language-action (VLA) models. Our experiments highlight the significant impact of scene clutter, lowering the performance of the policies, by as much as 34% and show that despite achieving similar average performance across the tasks, different VLA policies have unique vulnerabilities and a relatively low agreement on success scenarios. We further show that our clutter measure is an effective indicator of performance degradation and analyze the impact of distractors in terms of their quantity and occluding influence. At the end, we show that finetuning on enhanced data, although effective, does not equally remedy all negative impacts of clutter on performance.
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