# Pick Planning Strategies for Large-Scale Package Manipulation

arXiv: https://arxiv.org/abs/2309.13224 · HTML: https://mlsanity.com/paper/2309.13224/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mask Scoring R-CNN](https://mlsanity.com/paper/1903.00241/) (2019)
- [Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success](https://mlsanity.com/paper/2305.10272/) (2023)

## Built on by (most cited first, 1 of 1)

- [Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction](https://mlsanity.com/paper/2506.09765/) (2025)

## Abstract (arXiv)

Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to market fluctuations. This extended abstract showcases a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which is used for picking and singulating up to 6 million packages per day and so far has manipulated over 2 billion packages. It describes the various heuristic methods developed over time and their successor, which utilizes a pick success predictor trained on real production data. To the best of the authors' knowledge, this work is the first large-scale deployment of learned pick quality estimation methods in a real production system.