# A Learning Approach to Robot-Agnostic Force-Guided High Precision Assembly

arXiv: https://arxiv.org/abs/2010.08052 · HTML: https://mlsanity.com/paper/2010.08052/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

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

- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility](https://mlsanity.com/paper/2309.16909/) (2023)
- [Blox-Net: Generative Design-for-Robot-Assembly Using VLM Supervision, Physics Simulation, and a Robot with Reset](https://mlsanity.com/paper/2409.17126/) (2024)
- [Flow-based Domain Randomization for Learning and Sequencing Robotic Skills](https://mlsanity.com/paper/2502.01800/) (2025)
- [Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study](https://mlsanity.com/paper/2204.12844/) (2022)
- [AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use](https://mlsanity.com/paper/2509.15153/) (2025)

## Abstract (arXiv)

In this work we propose a learning approach to high-precision robotic assembly problems. We focus on the contact-rich phase, where the assembly pieces are in close contact with each other. Unlike many learning-based approaches that heavily rely on vision or spatial tracking, our approach takes force/torque in task space as the only observation. Our training environment is robotless, as the end-effector is not attached to any specific robot. Trained policies can then be applied to different robotic arms without re-training. This approach can greatly reduce complexity to perform contact-rich robotic assembly in the real world, especially in unstructured settings such as in architectural construction. To achieve it, we have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which extends Ape-X DDPG with recurrency and makes two structural improvements on prioritized experience replay. Our results show that RD2 is able to solve two fundamental high-precision assembly tasks, lap-joint and peg-in-hole, and outperforms two state-of-the-art algorithms, Ape-X DDPG and PPO with LSTM. We have successfully evaluated our robot-agnostic policies on three robotic arms, Kuka KR60, Franka Panda, and UR10, in simulation. The video presenting our experiments is available at https://sites.google.com/view/rd2-rl