# Towards Robotic Assembly by Predicting Robust, Precise and Task-oriented Grasps

arXiv: https://arxiv.org/abs/2011.02462 · HTML: https://mlsanity.com/paper/2011.02462/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects](https://mlsanity.com/paper/2211.04604/) (2022)
- [CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation](https://mlsanity.com/paper/2109.09163/) (2021)
- [FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback](https://mlsanity.com/paper/2303.07997/) (2023)

## Abstract (arXiv)

Robust task-oriented grasp planning is vital for autonomous robotic precision assembly tasks. Knowledge of the objects' geometry and preconditions of the target task should be incorporated when determining the proper grasp to execute. However, several factors contribute to the challenges of realizing these grasps such as noise when controlling the robot, unknown object properties, and difficulties modeling complex object-object interactions. We propose a method that decomposes this problem and optimizes for grasp robustness, precision, and task performance by learning three cascaded networks. We evaluate our method in simulation on three common assembly tasks: inserting gears onto pegs, aligning brackets into corners, and inserting shapes into slots. Our policies are trained using a curriculum based on large-scale self-supervised grasp simulations with procedurally generated objects. Finally, we evaluate the performance of the first two tasks with a real robot where our method achieves 4.28mm error for bracket insertion and 1.44mm error for gear insertion.