Robot packing improves by planning with gripper and real-time control
Gripper-Aware Automatic Dense Packing of Irregular Objects
Robotics
Summary
Packing odd-shaped objects tightly is hard for robots because they usually only think about the objects and not their own hands. The authors designed a system where the robot plans moves while considering its gripper's shape and keeps checking with sensors to avoid mistakes. The robot also carefully pushes items together after placing them to reduce gaps. This method helps the robot pack objects more tightly and accurately in real-world tests.
What this means in practice
- •For warehouse automation teams: Enable robots to pack irregularly shaped items tightly and reliably by planning placements that account for the gripper and by using force feedback to avoid errors.
- •For robotics engineers in logistics: Use real-time sensing and gripper-aware models to improve robot object packing in cluttered or irregular environments for material handling applications.
Authors
Tianhao Qin, Connor McCann, Berk Calli, Jing Xiao
Abstract
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.