# Critically fast pick-and-place with suction cups

arXiv: https://arxiv.org/abs/1809.03151 · HTML: https://mlsanity.com/paper/1809.03151/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

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

- [SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning](https://mlsanity.com/paper/2512.00062/) (2025)
- [Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation](https://mlsanity.com/paper/2603.20658/) (2026)
- [Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing](https://mlsanity.com/paper/2603.07800/) (2026)
- [SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning](https://mlsanity.com/paper/2608.09138/) (2026)

## Abstract (arXiv)

Fast robotics pick-and-place with suction cups is a crucial component in the current development of automation in logistics (factory lines, e-commerce, etc.). By "critically fast" we mean the fastest possible movement for transporting an object such that it does not slip or fall from the suction cup. The main difficulties are: (i) handling the contact between the suction cup and the object, which fundamentally involves kinodynamic constraints; and (ii) doing so at a low computational cost, typically a few hundreds of milliseconds. To address these difficulties, we propose (a) a model for suction cup contacts, (b) a procedure to identify the contact stability constraint based on that model, and (c) a pipeline to parameterize, in a time-optimal manner, arbitrary geometric paths under the identified contact stability constraint. We experimentally validate the proposed pipeline on a physical robot system: the cycle time for a typical pick-and-place task was less than 5 seconds, planning and execution times included. The full pipeline is released as open-source for the robotics community.