ActiveGrasp：基于校准能量模型的分布信息引导主动抓取
ActiveGrasp: Information-Guided Active Grasping with Calibrated Energy-based Model
把抓取位姿分布建模为 SE(3) 流形上的校准能量模型，并用其条件分布的信息增益来驱动下一最佳视角选择，为主动感知/主动重建中的视角规划提供了一个可复用的「分布级」信息度量思路。
原文依据 The next best view is selected by estimating the information gain for grasp from the calibrated distribution conditioned on the reconstructed environment
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在密集杂乱环境中进行抓取对机器人而言是一项具有挑战性的任务。此前的方法试图通过在生成抓取位姿之前主动采集多个视角来解决这一问题。然而，这些方法要么忽视了抓取分布对信息增益估计的重要性，要么依赖于抓取分布的投影，从而忽略了抓取位姿在 SE(3) 流形上的结构。为应对这些挑战，我们提出了一种用于抓取位姿生成的校准能量模型，以及一种从抓取分布估计信息增益的主动视角选择方法。我们的能量模型刻画了抓取分布在 SE(3) 流形上的多模态特性。能量水平被校准到抓取的成功率，从而使预测分布与真实分布对齐。下一最佳视角通过在以重建环境为条件的校准分布上估计抓取的信息增益来选取，这能够高效地驱动机器人探索目标物体上可负担抓取的部位。在仿真环境和真实机器人平台上的实验表明，与先前的最优模型相比，我们的模型能够在有限的视角预算下成功抓取杂乱环境中的物体。我们的仿真环境可作为未来主动抓取研究的可复现平台。论文公开时，我们将公开源代码。
英文原文摘要 1497 字符
Grasping in a densely cluttered environment is a challenging task for robots. Previous methods tried to solve this problem by actively gathering multiple views before grasp pose generation. However, they either overlooked the importance of the grasp distribution for information gain estimation or relied on the projection of the grasp distribution, which ignores the structure of grasp poses on the SE(3) manifold. To tackle these challenges, we propose a calibrated energy-based model for grasp pose generation and an active view selection method that estimates information gain from grasp distribution. Our energy-based model captures the multi-modality nature of grasp distribution on the SE(3) manifold. The energy level is calibrated to the success rate of grasps so that the predicted distribution aligns with the real distribution. The next best view is selected by estimating the information gain for grasp from the calibrated distribution conditioned on the reconstructed environment, which could efficiently drive the robot to explore affordable parts of the target object. Experiments on simulated environments and real robot setups demonstrate that our model could successfully grasp objects in a cluttered environment with limited view budgets compared to previous state-of-the-art models. Our simulated environment can serve as a reproducible platform for future research on active grasping. The source code of our paper will be made public when the paper is released to the public.