Robotic grasping of 3D deformable objects is critical for real-world applications such as food handling and robotic surgery. Unlike rigid and articulated objects, 3D deformable objects have infinite degrees of freedom. Fully defining their state requires 3D deformation and stress fields, which are exceptionally difficult to analytically compute or experimentally measure. Thus, evaluating grasp candidates for grasp planning typically requires accurate, but slow 3D finite element method (FEM) simulation. Sampling-based grasp planning is often impractical, as it requires evaluation of a large number of grasp candidates. Gradient-based grasp planning can be more efficient, but requires a differentiable model to synthesize optimal grasps from initial candidates. Differentiable FEM simulators may fill this role, but are typically no faster than standard FEM. In this work, we propose learning a predictive graph neural network (GNN), DefGraspNets, to act as our differentiable model. We train DefGraspNets to predict 3D stress and deformation fields based on FEM-based grasp simulations. DefGraspNets not only runs up to 1500 times faster than the FEM simulator, but also enables fast gradient-based grasp optimization over 3D stress and deformation metrics. We design DefGraspNets to align with real-world grasp planning practices and demonstrate generalization across multiple test sets, including real-world experiments.

In recent years, there is an increasing interest in high school robotics extracurriculars such as robotics clubs and robotics competitions. The growing demand is a result of more ubiquitous open-source software and affordable off-the-shelf hardware kits, which significantly help lower the barrier for entry-level robotics hobbyists. In this project, we present an open-source, low-cost, and lightweight robotic manipulator designed and developed by a high school researcher under the guidance of a university faculty and a Ph.D. student. We believe the presented project is suitable for high school robotics research and educational activities. Our open-source package consists of mechanical design models, mechatronics specifications, and software program source codes. The mechanical design models include CAD (Computer Aided Design) files that are ready for prototyping (3D printing technology) and serve as an assembly guide accommodated with a complete bill of materials. Electrical wiring diagrams and low-level controllers are documented in detail as part of the open-source software package. The educational objective of this project is to enable high school student teams to replicate and build a robotic manipulator. The engineering experience that high school students acquire in the proposed project is full-stack, including mechanical design, mechatronics, and programming. The project significantly enriches their hands-on engineering experience in a project-based environment. Throughout this project, we discovered that the high school researcher was able to apply multidisciplinary knowledge from K-12 STEM courses to build the robotic manipulator. The researcher was able to go through a system engineering design and development process and obtain skills to use professional engineering tools including SolidWorks and Arduino microcontrollers.