Mobile Manipulation (MoMa) systems incorporate the benefits of mobility and dexterity, due to the enlarged space in which they can move and interact with their environment. However, even when equipped with onboard sensors, e.g., an embodied camera, extracting task-relevant visual information in unstructured and cluttered environments, such as households, remains challenging. In this work, we introduce an active perception pipeline for mobile manipulators to generate motions that are informative toward manipulation tasks, such as grasping in unknown, cluttered scenes. Our proposed approach, ActPerMoMa, generates robot paths in a receding horizon fashion by sampling paths and computing path-wise utilities. These utilities trade-off maximizing the visual Information Gain (IG) for scene reconstruction and the task-oriented objective, e.g., grasp success, by maximizing grasp reachability. We show the efficacy of our method in simulated experiments with a dual-arm TIAGo++ MoMa robot performing mobile grasping in cluttered scenes with obstacles. We empirically analyze the contribution of various utilities and parameters, and compare against representative baselines both with and without active perception objectives. Finally, we demonstrate the transfer of our mobile grasping strategy to the real world, indicating a promising direction for active-perceptive MoMa.

翻译：移动操作（MoMa）系统融合了移动性与灵巧性的优势，得益于其扩展的运动与交互空间。然而，即便配备机载传感器（如具身摄像头），在家庭等非结构化杂乱环境中提取任务相关的视觉信息仍具挑战。本文提出一种面向移动操作机器人的有源感知流水线，旨在生成对操作任务（如在未知杂乱场景中抓取）具有信息增益的运动策略。所提方法ActPerMoMa通过滑动视界方式采样路径并计算路径级效用函数，从而生成机器人运动轨迹。该效用函数在最大化场景重建的视觉信息增益（IG）与任务导向目标（如通过最大化抓取可达性实现抓取成功率）之间进行权衡。我们在双机械臂TIAGo++ MoMa机器人于障碍物杂乱场景中进行移动抓取的仿真实验中验证了方法的有效性，通过实证分析各效用函数与参数贡献，并与含/不含主动感知目标的代表性基线方法进行对比。最后，我们展示了移动抓取策略向真实世界的迁移能力，为有源感知MoMa系统指明了具有前景的发展方向。