抓爪感知的不规则物体自动密集装箱
Gripper-Aware Automatic Dense Packing of Irregular Objects
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中文总结 AI 辅助
提出一种集成感知、抓爪感知放置优化与力引导执行的闭环流水线，在真实机械臂上实现不规则物体密集装箱，并通过消融和基线对比验证其有效性。
中文摘要 AI 辅助
自动密集装箱在仓储作业中被广泛需求，但在机器人操作中仍是一个基础性挑战。现有的不规则物体装箱研究主要针对具有理想接触的仿真环境，将物体视为孤立的刚体。抓爪通常以离散的、事后可行性检查的形式介入（如果被考虑的话），而执行过程中累积的感知和接触漂移则未被处理。我们提出了一种闭环流水线，在真实机械臂上集成了感知、抓爪感知的放置优化和力引导执行。优化器将物体与抓爪表示为一个由分层球树构成的单一复合体。它在CMA-ES框架内于GPU上搜索五个自由度，垂直坐标基于当前高度图解析地确定。在执行过程中，力监控的垂直下降在首次接触时停止。释放后的压实推挤操作随后闭合了抓爪感知规划留下的残余横向间隙。在每次放置之间重新感知容器，以避免漂移累积。我们在Franka Emika Panda机器人上验证了该系统，装箱一组3D打印的平面、曲面和凹面物体，以及一个YCB物体子集。一项消融研究隔离了抓爪感知优化、压实推挤和网格派生几何对端到端成功率、达到的密度和计算成本的贡献。我们进一步与高度图最小化方法作为先前不规则物体装箱工作的基线代表进行了基准比较。
英文摘要
Automatic dense packing is widely desired in warehouse operations but remains a fundamental challenge in robotic manipulation. Existing work on irregular-object packing largely targets simulation with idealized contact, treating the object as an isolated rigid body. The gripper often enters as a discrete, post-hoc feasibility check, if considered at all, and the perception and contact drift accumulated during execution are not addressed. We present a closed-loop pipeline that integrates perception, gripper-aware placement optimization, and force-guided execution on a real manipulator. The optimizer represents the object together with the gripper as a single composite body of hierarchical sphere trees. It searches over five degrees of freedom on a GPU within a CMA-ES framework, with the vertical coordinate grounded analytically against the current heightmap. During execution, a force-monitored vertical descent stops on first contact. A post-release consolidation push then closes the residual lateral clearance that gripper-aware planning leaves behind. The container is re-perceived between placements so that drift does not accumulate. We validate the system on a Franka Emika Panda robot packing a 3D-printed set of flat, curved, and concave objects, and a YCB object subset. An ablation study isolates the contribution of gripper-aware optimization, the consolidation push, and mesh-derived geometry to end-to-end success, achieved density, and computational cost. We further benchmark against the heightmap-minimization method as a baseline representative of prior irregular-object packing work.
发表机构
- Worcester Polytechnic Institute（伍斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。
