# Robots Assembling Machines: Learning from the World Robot Summit 2018 Assembly Challenge

arXiv: https://arxiv.org/abs/1911.05884 · HTML: https://mlsanity.com/paper/1911.05884/

## Key facts

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

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

- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [6D Robotic Assembly Based on RGB-only Object Pose Estimation](https://mlsanity.com/paper/2208.12986/) (2022)
- [Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks](https://mlsanity.com/paper/2103.05140/) (2021)
- [PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance](https://mlsanity.com/paper/2106.03220/) (2021)
- [Off-the-shelf bin picking workcell with visual pose estimation: A case study on the world robot summit 2018 kitting task](https://mlsanity.com/paper/2309.16221/) (2023)
- [Optimal decision making in robotic assembly and other trial-and-error tasks](https://mlsanity.com/paper/2301.10846/) (2023)

## Abstract (arXiv)

The Industrial Assembly Challenge at the World Robot Summit was held in 2018 to showcase the state-of-the-art of autonomous manufacturing systems. The challenge included various tasks, such as bin picking, kitting, and assembly of standard industrial parts into 2D and 3D assemblies. Some of the tasks were only revealed at the competition itself, representing the challenge of "level 5" automation, i. e., programming and setting up an autonomous assembly system in less than one day. We conducted a survey among the teams that participated in the challenge and investigated aspects such as team composition, development costs, system setups as well as the teams' strategies and approaches. An analysis of the survey results reveals that the competitors have been in two camps: those constructing conventional robotic work cells with off-the-shelf tools, and teams who mostly relied on custom-made end effectors and novel software approaches in combination with collaborative robots. While both camps performed reasonably well, the winning team chose a middle ground in between, combining the efficiency of established play-back programming with the autonomy gained by CAD-based object detection and force control for assembly operations.