学会看与做：面向机器人操作的任务感知虚拟视角探索
Learning to See and Act: Task-Aware Virtual View Exploration for Robotic Manipulation
把「选视角」本身建模为可学习策略，并在重建的场景表示上按所选虚拟相机重渲染观测，这为视觉研究者提供了一条绕开固定相机与遮挡的通用思路：用可微/可重渲染的场景表示做主动感知，而不是被动接受输入图像。
原文依据 learns to select task-relevant virtual camera viewpoints and dynamically re-render observations from a reconstructed scene representation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期面向多任务机器人操作的视觉语言动作（VLA）模型往往依赖固定的相机配置与共享的视觉编码器，这限制了它们在遮挡情形下以及跨任务迁移时的表现。为应对这些挑战，我们提出任务感知虚拟视角探索（TVVE）框架，该框架学习选择与任务相关的虚拟相机视角，并利用所选视角从重建的场景表示中动态重渲染观测。为实现高效的视角选择，我们在伪环境中训练了一个探索策略。此外，我们引入了任务感知混合专家（TaskMoE）视觉编码器，将视觉特征路由到任务专用的专家，从而缓解多任务学习中的干扰。为评估分布偏移下的鲁棒性，我们构建了 RLBench-OG，这是一个包含视觉扰动与相机位姿变化的分布外基准。在 RLBench 与 RLBench-OG 上的实验表明，TVVE 取得了高于强基线的成功率，真实机器人实验进一步证实了其对视觉干扰与未见指令的鲁棒性。代码与可视化结果见：https://hcplab-sysu.github.io/TAVP。
英文原文摘要 1228 字符
Recent vision-language-action (VLA) models for multi-task robot manipulation often rely on fixed camera setups and shared visual encoders, which limit their performance under occlusions and during cross-task transfer. To address these challenges, we propose Task-aware Virtual View Exploration (TVVE), a framework that learns to select task-relevant virtual camera viewpoints and dynamically re-render observations from a reconstructed scene representation using the selected viewpoints. To enable efficient view selection, we train an exploration policy in a pseudo-environment. In addition, we introduce a Task-aware Mixture-of-Experts (TaskMoE) visual encoder that routes visual features to task-specialized experts, mitigating interference in multi-task learning. To evaluate robustness under distribution shifts, we construct RLBench-OG, an out-of-distribution benchmark with visual perturbations and camera pose variations. Experiments on RLBench and RLBench-OG demonstrate that TVVE achieves higher success rates than strong baselines, while real-robot experiments further confirm its robustness to visual disturbances and unseen instructions. Code and visualizations are available at: https://hcplab-sysu.github.io/TAVP.