# Learning Human-Inspired Force Strategies for Robotic Assembly

arXiv: https://arxiv.org/abs/2303.12440 · HTML: https://mlsanity.com/paper/2303.12440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 22 Mar 2023.

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

- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards](https://mlsanity.com/paper/1906.05841/) (2019)
- [Deep Reinforcement Learning for High Precision Assembly Tasks](https://mlsanity.com/paper/1708.04033/) (2017)
- [A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control](https://mlsanity.com/paper/1805.08576/) (2018)
- [Generative Choreography using Deep Learning](https://mlsanity.com/paper/1605.06921/) (2016)
- [Contact Skill Imitation Learning for Robot-Independent Assembly Programming](https://mlsanity.com/paper/1908.06272/) (2019)
- [Virtual Forward Dynamics Models for Cartesian Robot Control](https://mlsanity.com/paper/2009.11888/) (2020)

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

- [Phase-Conditioned Imitation Learning with Autonomous Failure Recovery for Robust Deformable Object Manipulation](https://mlsanity.com/paper/2605.29407/) (2026)

## Abstract (arXiv)

The programming of robotic assembly tasks is a key component in manufacturing and automation. Force-sensitive assembly, however, often requires reactive strategies to handle slight changes in positioning and unforeseen part jamming. Learning such strategies from human performance is a promising approach, but faces two common challenges: the handling of low part clearances which is difficult to capture from demonstrations and learning intuitive strategies offline without access to the real hardware. We address these two challenges by learning probabilistic force strategies from data that are easily acquired offline in a robot-less simulation from human demonstrations with a joystick. We combine a Long Short Term Memory (LSTM) and a Mixture Density Network (MDN) to model human-inspired behavior in such a way that the learned strategies transfer easily onto real hardware. The experiments show a UR10e robot that completes a plastic assembly with clearances of less than 100 micrometers whose strategies were solely demonstrated in simulation.