# Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks

arXiv: https://arxiv.org/abs/2103.05140 · HTML: https://mlsanity.com/paper/2103.05140/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 8 Mar 2021.

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

- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Camera-to-Robot Pose Estimation from a Single Image](https://mlsanity.com/paper/1911.09231/) (2019)
- [Analysis and Observations from the First Amazon Picking Challenge](https://mlsanity.com/paper/1601.05484/) (2016)
- [Deep Reinforcement Learning for High Precision Assembly Tasks](https://mlsanity.com/paper/1708.04033/) (2017)
- [Learning Robotic Assembly from CAD](https://mlsanity.com/paper/1803.07635/) (2018)
- [Learning Dense Rewards for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2011.08458/) (2020)
- [Robots Assembling Machines: Learning from the World Robot Summit 2018 Assembly Challenge](https://mlsanity.com/paper/1911.05884/) (2019)
- [Autonomous Industrial Assembly using Force, Torque, and RGB-D sensing](https://mlsanity.com/paper/2002.02580/) (2020)

## Built on by (most cited first, 7 of 7)

- [HomeRobot: Open-Vocabulary Mobile Manipulation](https://mlsanity.com/paper/2306.11565/) (2023)
- [You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration](https://mlsanity.com/paper/2201.12716/) (2022)
- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision](https://mlsanity.com/paper/2210.15206/) (2022)
- [Situated Instruction Following](https://mlsanity.com/paper/2407.12061/) (2024)
- [Optimal decision making in robotic assembly and other trial-and-error tasks](https://mlsanity.com/paper/2301.10846/) (2023)
- [AlphaNIST: Emergent Assembly Skills from Binary Task Rewards](https://mlsanity.com/paper/2610.13354/) (2026)

## Abstract (arXiv)

In recent years, many learning based approaches have been studied to realize robotic manipulation and assembly tasks, often including vision and force/tactile feedback. However, it remains frequently unclear what is the baseline state-of-the-art performance and what are the bottleneck problems. In this work, we evaluate some off-the-shelf (OTS) industrial solutions on a recently introduced benchmark, the National Institute of Standards and Technology (NIST) Assembly Task Boards. A set of assembly tasks are introduced and baseline methods are provided to understand their intrinsic difficulty. Multiple sensor-based robotic solutions are then evaluated, including hybrid force/motion control and 2D/3D pattern matching algorithms. An end-to-end integrated solution that accomplishes the tasks is also provided. The results and findings throughout the study reveal a few noticeable factors that impede the adoptions of the OTS solutions: expertise dependent, limited applicability, lack of interoperability, no scene awareness or error recovery mechanisms, and high cost. This paper also provides a first attempt of an objective benchmark performance on the NIST Assembly Task Boards as a reference comparison for future works on this problem.