GOMP-ST: Grasp Optimized Motion Planning for Suction Transport

TL;DR

GOMP-ST is a novel algorithm that combines deep learning and optimization to plan high-speed suction transport motions, reducing transport time while preventing suction cup failure in robotic grasping.

Contribution

It introduces a learning-based constraint integrated into a motion planner to account for real-world suction cup deformation and failure conditions.

Findings

Reduced transport times by 16% to 58%.

Successfully avoided suction cup failure in 420 experiments.

Integrated real-world effects into motion planning.

Abstract

Suction cup grasping is very common in industry, but moving too quickly can cause suction cups to detach, causing drops or damage. Maintaining a suction grasp throughout a high-speed motion requires balancing suction forces against inertial forces while the suction cups deform under strain. In this paper, we consider Grasp Optimized Motion Planning for Suction Transport (GOMP-ST), an algorithm that combines deep learning with optimization to decrease transport time while avoiding suction cup failure. GOMP-ST first repeatedly moves a physical robot, vacuum gripper, and a sample object, while measuring pressure with a solid-state sensor to learn critical failure conditions. Then, these are integrated as constraints on the accelerations at the end-effector into a time-optimizing motion planner. The resulting plans incorporate real-world effects such as suction cup deformation that are difficult to model analytically. In GOMP-ST, the learned constraint, modeled with a neural network, is linearized using Autograd and integrated into a sequential quadratic program optimization. In 420 experiments with a physical UR5 transporting objects ranging from 1.3 to 1.7 kg, we compare GOMP-ST to baseline optimizing motion planners. Results suggest that GOMP-ST can avoid suction cup failure while decreasing transport times from 16% to 58%. For code, video, and datasets, see https://sites.google.com/view/gomp-st.