具备策略性视点选择的视角无关操作策略
Viewpoint-Agnostic Manipulation Policies with Strategic Vantage Selection
AI总结：
针对单视角训练的视觉操作策略视角变化时性能下降的问题，提出Vantage视角选择框架，将相机放置建模为连续空间信息增益优化问题，仅需少量微调即可显著提升不同策略在视角偏移下的成功率。
摘要：
由于基于视觉的操作策略通常是利用从单一视角采集的数据训练的，在部署过程中视角发生变化时，其性能会出现下降。简单地聚合来自大量随机视角的演示数据不仅成本高昂，还已被证实会 destabilize 学习过程，因为过度的视觉多样性会起到噪声的作用。我们提出Vantage（一种视角选择框架），它能通过少量经过策略性筛选的相机位姿对任意预训练策略进行微调，从而实现视角无关的行为表现。Vantage无需对视角进行成本高昂的暴力搜索，而是将相机放置问题建模为连续空间中的信息增益优化问题。该方法在探索新位姿与利用有前景的位姿之间取得平衡，同时还提供了关于收敛性和鲁棒性的理论保证。在各类操作任务和策略架构中，仅需少量微调步骤，Vantage在视角偏移场景下的成功率就始终优于固定视角、网格视角或随机数据选择策略。在模拟和真实环境中开展的实验表明，Vantage将扩散策略（diffusion policies）的任务成功率提升了25%，并且在动态相机设置下也能带来稳定的性能增益。
英文摘要：
Since vision-based manipulation policies are typically trained from data gathered from a single viewpoint, their performance drops when the view changes during deployment. Naively aggregating demonstrations from numerous random views is not only costly but also known to destabilize learning, as excessive visual diversity acts as noise. We present Vantage, a viewpoint selection framework to fine-tune any pre-trained policy on a small, strategically set of camera poses to induce viewpoint-agnostic behavior. Instead of relying on costly brute-force search over viewpoints, Vantage formulates camera placement as an information gain optimization problem in a continuous space. This approach balances exploration of novel poses with exploitation of promising ones, while also providing theoretical guarantees about convergence and robustness. Across manipulation tasks and policy families, Vantage consistently improves success under viewpoint shifts compared to fixed, grid, or random data selection strategies with only a handful of fine-tuning steps. Experiments conducted on simulated and real-world setups show that Vantage increases the task success rate by 25% for diffusion policies, and yields robust gains in dynamic-camera settings.
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。
