DSPv2：改进的密集策略，用于有效且可泛化的全身移动操作
DSPv2: Improved Dense Policy for Effective and Generalizable Whole-body Mobile Manipulation
AI总结：
DSPv2通过融合3D空间与多视角2D语义特征，并扩展密集策略范式，实现了全身移动操作的有效学习与泛化，性能显著优于现有方法。
摘要：
通过模仿学习全身移动操作对于将机器人技能泛化到多样环境和复杂任务至关重要。然而，这一目标受到重大挑战的阻碍，特别是在有效处理复杂观测、实现稳健泛化以及生成连贯动作方面。为解决这些问题，我们提出了DSPv2，一种新颖的策略架构。DSPv2引入了一种有效的编码方案，将3D空间特征与多视角2D语义特征对齐。这种融合使策略能够实现广泛的泛化，同时保留精确控制所需的细粒度感知。此外，我们将密集策略范式扩展到全身移动操作领域，展示了其在为全身机器人平台生成连贯且精确动作方面的有效性。大量实验表明，我们的方法在任务性能和泛化能力上均显著优于现有方法。项目页面可在以下网址获取：https://selen-suyue.github.io/DSPv2Net/。
英文摘要：
Learning whole-body mobile manipulation via imitation is essential for generalizing robotic skills to diverse environments and complex tasks. However, this goal is hindered by significant challenges, particularly in effectively processing complex observation, achieving robust generalization, and generating coherent actions. To address these issues, we propose DSPv2, a novel policy architecture. DSPv2 introduces an effective encoding scheme that aligns 3D spatial features with multi-view 2D semantic features. This fusion enables the policy to achieve broad generalization while retaining the fine-grained perception necessary for precise control. Furthermore, we extend the Dense Policy paradigm to the whole-body mobile manipulation domain, demonstrating its effectiveness in generating coherent and precise actions for the whole-body robotic platform. Extensive experiments show that our method significantly outperforms existing approaches in both task performance and generalization ability. Project page is available at: https://selen-suyue.github.io/DSPv2Net/.
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。
