# Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction

arXiv: https://arxiv.org/abs/2506.09765 · HTML: https://mlsanity.com/paper/2506.09765/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains](https://mlsanity.com/paper/2212.08333/) (2022)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Analysis and Observations from the First Amazon Picking Challenge](https://mlsanity.com/paper/1601.05484/) (2016)
- [M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place](https://mlsanity.com/paper/2311.00926/) (2023)
- [SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping](https://mlsanity.com/paper/2103.12311/) (2021)
- [Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success](https://mlsanity.com/paper/2305.10272/) (2023)
- [Pick Planning Strategies for Large-Scale Package Manipulation](https://mlsanity.com/paper/2309.13224/) (2023)

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

- [Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing](https://mlsanity.com/paper/2603.07800/) (2026)

## Abstract (arXiv)

Warehouse automation plays a pivotal role in enhancing operational efficiency, minimizing costs, and improving resilience to workforce variability. While prior research has demonstrated the potential of machine learning (ML) models to increase picking success rates in large-scale robotic fleets by prioritizing high-probability picks and packages, these efforts primarily focused on predicting success probabilities for picks sampled using heuristic methods. Limited attention has been given, however, to leveraging data-driven approaches to directly optimize sampled picks for better performance at scale. In this study, we propose an ML-based framework that predicts transform adjustments as well as improving the selection of suction cups for multi-suction end effectors for sampled picks to enhance their success probabilities. The framework was integrated and evaluated in test workcells that resemble the operations of Amazon Robotics' Robot Induction (Robin) fleet, which is used for package manipulation. Evaluated on over 2 million picks, the proposed method achieves a 20% reduction in pick failure rates compared to a heuristic-based pick sampling baseline, demonstrating its effectiveness in large-scale warehouse automation scenarios.