ARMFUL: Learning Whole-Arm Grasp Configurations
for Single-Arm Pickup of Large Objects

Many everyday objects, such as storage boxes, baskets, and chairs, are too large or heavy for a gripper to grasp, yet a single arm can still pick them up by wrapping around and supporting them. Such grasps are hard to learn: wrapping motions are difficult to teleoperate, and existing datasets contain little whole-arm pickup data. We introduce WAG (Whole-Arm Grasp Dataset), the first large-scale dataset for single-arm pickup of large objects, with 1.7 million certified joint configurations across 978 objects. We sample configurations directly in joint space, execute each through a squeeze-and-lift primitive in simulation, and certify survivors under active shaking and external forces. To demonstrate the utility of WAG and accomplish the whole-arm pickup task, we develop ARMFUL, a whole-arm pickup pipeline that pairs a diffusion model trained on WAG, generating whole-arm grasps from the object's point cloud, with a motion planner that reaches them collision-free before the same squeeze-and-lift execution. In simulation, ARMFUL reaches 80.4% lift success on seen and 72.8% on unseen objects, and it lifts the object in 70 of 100 trials on a physical Franka arm across 10 IKEA objects.