# GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport

arXiv: https://arxiv.org/abs/2110.15326 · HTML: https://mlsanity.com/paper/2110.15326/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation](https://mlsanity.com/paper/2412.12089/) (2024)
- [That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation](https://mlsanity.com/paper/2210.01116/) (2022)
- [Reinforcement Learning Control for Autonomous Hydraulic Material Handling Machines with Underactuated Tools](https://mlsanity.com/paper/2410.05093/) (2024)
- [MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy](https://mlsanity.com/paper/2210.10842/) (2022)

## Abstract (arXiv)

High-speed motions in pick-and-place operations are critical to making robots cost-effective in many automation scenarios, from warehouses and manufacturing to hospitals and homes. However, motions can be too fast -- such as when the object being transported has an open-top, is fragile, or both. One way to avoid spills or damage, is to move the arm slowly. We propose an alternative: Grasp-Optimized Motion Planning for Fast Inertial Transport (GOMP-FIT), a time-optimizing motion planner based on our prior work, that includes constraints based on accelerations at the robot end-effector. With GOMP-FIT, a robot can perform high-speed motions that avoid obstacles and use inertial forces to its advantage. In experiments transporting open-top containers with varying tilt tolerances, whereas GOMP computes sub-second motions that spill up to 90% of the contents during transport, GOMP-FIT generates motions that spill 0% of contents while being slowed by as little as 0% when there are few obstacles, 30% when there are high obstacles and 45-degree tolerances, and 50% when there 15-degree tolerances and few obstacles. Videos and more at: https://berkeleyautomation.github.io/gomp-fit/.