# Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge

arXiv: https://arxiv.org/abs/1709.06283 · HTML: https://mlsanity.com/paper/1709.06283/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2017.

## Built on by (most cited first, 19 of 19)

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- [Synthetic-to-Real Domain Adaptation using Contrastive Unpaired Translation](https://mlsanity.com/paper/2203.09454/) (2022)
- [CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation](https://mlsanity.com/paper/2112.04966/) (2021)
- [Mechanical Search on Shelves using a Novel "Bluction" Tool](https://mlsanity.com/paper/2201.08968/) (2022)
- [Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping](https://mlsanity.com/paper/2404.06277/) (2024)
- [Semi Supervised Deep Quick Instance Detection and Segmentation](https://mlsanity.com/paper/2101.06405/) (2021)
- [Predicting Physical Object Properties from Video](https://mlsanity.com/paper/2206.00930/) (2022)
- [Opening Articulated Structures in the Real World](https://mlsanity.com/paper/2402.17767/) (2024)
- [Cross-Domain Knowledge Distillation for Low-Resolution Human Pose Estimation](https://mlsanity.com/paper/2405.11448/) (2024)
- [MultiGraspNet: A Multitask 3D Vision Model for Multi-gripper Robotic Grasping](https://mlsanity.com/paper/2602.06504/) (2026)
- [Visual Foresight for Robotic Stow: A Diffusion-Based World Model from Sparse Snapshots](https://mlsanity.com/paper/2602.13347/) (2026)
- [CAD-Prompted SAM3: Geometry-Conditioned Instance Segmentation for Industrial Objects](https://mlsanity.com/paper/2602.20551/) (2026)

## Abstract (arXiv)

The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. This paper presents the design of our custom-built, cost-effective, Cartesian robot system Cartman, which won first place in the competition finals by stowing 14 (out of 16) and picking all 9 items in 27 minutes, scoring a total of 272 points. We highlight our experience-centred design methodology and key aspects of our system that contributed to our competitiveness. We believe these aspects are crucial to building robust and effective robotic systems.