This article presentsSim-Suction, a robust object-aware suction grasp policy for mobile manipulation platforms with dynamic camera viewpoints, designed to pick up unknown objects from cluttered environments. Suction grasp policies typically employ data-driven approaches, necessitating large-scale, accurately-annotated suction grasp datasets. However, the generation of suction grasp datasets in cluttered environments remains underexplored, leaving uncertainties about the relationship between the object of interest and its surroundings. To address this, we propose a benchmark synthetic dataset,Sim-Suction-Dataset, comprising 500 cluttered environments with 3.2 million annotated suction grasp poses. The efficientSim-Suction-Datasetgeneration process provides novel insights by combining analytical models with dynamic physical simulations to create fast and accurate suction grasp pose annotations. We introduceSim-Suction-Pointnetto generate robust 6-D suction grasp poses by learning point-wise affordances from theSim-Suction-Dataset, leveraging the synergy of zero-shot text-to-segmentation. Real-world experiments for picking up all objects demonstrate thatSim-Suction-Pointnetachieves success rates of 96.76%, 94.23%, and 92.39% on cluttered level 1 objects (prismatic shape), cluttered level 2 objects (more complex geometry), and cluttered mixed objects, respectively.

This paper describes the development of a modular end-effector system (MEES) for autonomous robotic maintenance and repair tasks. The design consists of the following major components: Robot Side Mating Socket Module (RSMS), End-Effector Side Mating Socket Module (EEMS), the Modular Camera System (MCS), and Tool Holder/Changer unit. Multiple prototypes for each component have been manufactured, tested, and evaluated resulting in the final concept. Existing robotic tool-changer systems on the market were evaluated and features were built into the Modular End-Effector System to overcome the current limitations of those systems. A notable advantage to the MEES is that it is a robot agnostic system and simply uses an ISO standard bolt mounting pattern to physically attached to the robot of choice along with an ethernet connection. No external cables are required that could restrain the workspace of the robot manipulator. Additionally, it is compatible with customized wrist-mounted sensors and end-effectors without any modification of the actual robot circuitry. The MEES is demonstrated working with three different end-effectors and two different robots.