标题： 学习看见和行动：用于机器人操作的任务感知视点规划 显示英文标题

标题： Learning to See and Act: Task-Aware View Planning for Robotic Manipulation

摘要： 最近的多任务机器人操作视觉-语言-动作（VLA）模型通常依赖于静态视角和共享的视觉编码器，这限制了3D感知并导致任务干扰，阻碍了鲁棒性和泛化能力。 在本工作中，我们提出了任务感知视角规划（TAVP），这是一种通过将主动视角规划与任务特定表示学习相结合来克服这些挑战的框架。 TAVP采用了一种高效的探索策略，该策略通过一个新颖的伪环境加速，以主动获取信息丰富的视角。 此外，我们引入了一个专家混合（MoE）视觉编码器，以解耦不同任务之间的特征，提升表示精度和任务泛化能力。 通过以任务感知的方式观察世界，TAVP生成了更完整和具有区分性的视觉表示，在各种操作挑战中表现出显著增强的动作预测能力。 在RLBench任务上的大量实验表明，我们提出的TAVP模型在性能上优于最先进的固定视角方法。 视觉结果和代码可在以下网址获取：https://hcplab-sysu.github.io/TAVP. 显示英文摘要

摘要： Recent vision-language-action (VLA) models for multi-task robotic manipulation commonly rely on static viewpoints and shared visual encoders, which limit 3D perception and cause task interference, hindering robustness and generalization. In this work, we propose Task-Aware View Planning (TAVP), a framework designed to overcome these challenges by integrating active view planning with task-specific representation learning. TAVP employs an efficient exploration policy, accelerated by a novel pseudo-environment, to actively acquire informative views. Furthermore, we introduce a Mixture-of-Experts (MoE) visual encoder to disentangle features across different tasks, boosting both representation fidelity and task generalization. By learning to see the world in a task-aware way, TAVP generates more complete and discriminative visual representations, demonstrating significantly enhanced action prediction across a wide array of manipulation challenges. Extensive experiments on RLBench tasks show that our proposed TAVP model achieves superior performance over state-of-the-art fixed-view approaches. Visual results and code are provided at: https://hcplab-sysu.github.io/TAVP.