# Toward Fast and Optimal Robotic Pick-and-Place on a Moving Conveyor

arXiv: https://arxiv.org/abs/1912.08009 · HTML: https://mlsanity.com/paper/1912.08009/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Learning Physically Realizable Skills for Online Packing of General 3D Shapes](https://mlsanity.com/paper/2212.02094/) (2022)
- [Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search](https://mlsanity.com/paper/2101.12241/) (2021)
- [Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces](https://mlsanity.com/paper/2110.02814/) (2021)
- [SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning](https://mlsanity.com/paper/2512.00062/) (2025)
- [An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning](https://mlsanity.com/paper/2409.13511/) (2024)
- [SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning](https://mlsanity.com/paper/2608.09138/) (2026)

## Abstract (arXiv)

Robotic pick-and-place (PnP) operations on moving conveyors find a wide range of industrial applications. In practice, simple greedy heuristics (e.g., prioritization based on the time to process a single object) are applied that achieve reasonable efficiency. We show analytically that, under a simplified telescoping robot model, these greedy approaches do not ensure time optimality of PnP operations. To address the shortcomings of classical solutions, we develop algorithms that compute optimal object picking sequences for a predetermined finite horizon. Employing dynamic programming techniques and additional heuristics, our methods scale to up to tens to hundreds of objects. In particular, the fast algorithms we develop come with running time guarantees, making them suitable for real-time PnP applications demanding high throughput. Extensive evaluation of our algorithmic solution over dominant industrial PnP robots used in real-world applications, i.e., Delta robots and Selective Compliance Assembly Robot Arm (SCARA) robots, shows that a typical efficiency gain of around 10-40% over greedy approaches can be realized.