为狭小空间抓取与运动规划提供可扩展的基准框架
A Framework for Joint Grasp and Motion Planning in Confined Spaces
- 构建20个难度递增的仿真场景，包含真实物体与预标注抓取点
- 两种基线规划器在复杂场景中表现随难度提升而下降，验证了挑战性梯度
- 开源工具链支持社区共建新场景，适合机器人感知与规划研究者
机器人抓取是各类机器人应用中的基础技能。现有大量研究聚焦于桌面场景下的抓取，主要挑战在于寻找合适的抓取位姿。本文关注物体处于狭小空间中的情形，此时机器人接近目标的过程成为主要挑战，催生了联合抓取与运动规划的方法。本文提出一个框架，包含20个系统性增加难度的基准场景、具有预计算抓取标注的真实物体模型，以及用于创建和共享更多场景的工具。我们还提供了两种基线规划器，并在这些场景上进行了评估，证明所设计的难度等级确实呈现有意义的渐进性。我们呼吁研究社区基于此框架开展工作，所有组件均以开源形式公开。
原文摘要 · Abstract (English)
Robotic grasping is a fundamental skill across all domains of robot applications. There is a large body of research for grasping objects in table-top scenarios, where finding suitable grasps is the main challenge. In this work, we are interested in scenarios where the objects are in confined spaces and hence particularly difficult to reach. Planning how the robot approaches the object becomes a major part of the challenge, giving rise to methods for joint grasp and motion planning. The framework proposed in this paper provides 20 benchmark scenarios with systematically increasing difficulty, realistic objects with precomputed grasp annotations, and tools to create and share more scenarios. We further provide two baseline planners and evaluate them on the scenarios, demonstrating that the proposed difficulty levels indeed offer a meaningful progression. We invite the research community to build upon this framework by making all components publicly available as open source.
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