ReViP: 通过视觉-本体感知再平衡缓解视觉语言动作模型中的虚假完成
ReViP: Mitigating False Completion in Vision-Language-Action Models with Vision-Proprioception Rebalance
AI总结：
ReViP通过引入视觉-本体感知再平衡机制，缓解视觉语言动作模型中的虚假完成问题，提升模型在扰动下的鲁棒性和任务成功率。
摘要：
视觉语言动作（VLA）模型通过结合视觉、语言和本体感知来预测动作，从而推动了机器人操作的发展。然而，先前的方法直接将本体信号与视觉语言特征融合，导致状态主导偏差和尽管可见执行失败的虚假完成。我们系统地分析了这种失败模式，将其归因于模态不平衡，即策略过度依赖内部状态进展而低估了视觉证据。为了解决这一问题，我们引入了首个虚假完成基准测试套件，包含八个任务和三种受控扰动（对象掉落、干扰物交换、重新布局）以全面评估虚假完成。此外，我们提出了ReViP，一种新的VLA框架，具有视觉-本体感知再平衡，以增强视觉 grounding 和扰动下的鲁棒性。关键见解是引入辅助的进度感知视觉线索，以自适应地调节语义感知与本体动态之间的耦合。具体而言，进度感知视觉线索通过外部任务阶段观察者提取，该观察者对实时观察进行任务相关推理，以驱动任务阶段特征的线性调节，增强环境意识并缓解状态驱动的错误。广泛的实验表明，ReViP有效缓解了虚假完成并提高了成功率，优于强大的VLA基线，在我们的套件上比π₀模型提高了26%，并在LIBERO、RoboTwin 2.0和真实世界评估中也产生了收益。
英文摘要：
Vision-Language-Action (VLA) models have advanced robotic manipulation by combining vision, language, and proprioception to predict actions. However, previous methods fuse proprioceptive signals directly with vision-language features, resulting in state-dominant bias and \textbf{false completions} despite visible execution failures. We systematically analyze this failure mode, attributing it to modality imbalance, where policies overly rely on internal state progression and underuse visual evidence. To address this, we introduce the first \textbf{False-Completion Benchmark Suite}, featuring eight tasks with three controlled perturbations (\emph{Object Drop}, \emph{Distractor Swap}, \emph{Relayout}) to comprehensively evaluate false completion. Moreover, we propose \textbf{ReViP}, a novel VLA framework with \textbf{Vi}sion-\textbf{P}roprioception \textbf{Re}balance to enhance visual grounding and robustness under perturbations. The key insight is to introduce auxiliary \emph{progress-aware visual cues} to adaptively modulate the coupling between semantic perception and proprioceptive dynamics. Specifically, progress-aware visual cues are extracted by an external Task-Stage Observer, which performs task-relevant reasoning on real-time observations to drive task-stage feature-wise linear modulation, enhancing environmental awareness and mitigating state-driven errors. Extensive experiments show that ReViP effectively mitigates false completion and improves success rates over strong VLA baselines, achieving a \textbf{26\%} gain over $π_0$ model on our suite, with gains extending to LIBERO, RoboTwin 2.0, and real-world evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
