# Transferable Force-Torque Dynamics Model for Peg-in-hole Task

arXiv: https://arxiv.org/abs/1912.00260 · HTML: https://mlsanity.com/paper/1912.00260/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2019.

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

- [MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation](https://mlsanity.com/paper/2310.16917/) (2023)
- [Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty](https://mlsanity.com/paper/2106.04306/) (2021)
- [Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist](https://mlsanity.com/paper/2402.18002/) (2024)
- [PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation](https://mlsanity.com/paper/2312.02531/) (2023)
- [Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space](https://mlsanity.com/paper/2405.01134/) (2024)
- [Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes](https://mlsanity.com/paper/2205.04297/) (2022)

## Abstract (arXiv)

We present a learning-based force-torque dynamics to achieve model-based control for contact-rich peg-in-hole task using force-only inputs. Learning the force-torque dynamics is challenging because of the ambiguity of the low-dimensional 6-d force signal and the requirement of excessive training data. To tackle these problems, we propose a multi-pose force-torque state representation, based on which a dynamics model is learned with the data generated in a sample-efficient offline fashion. In addition, by training the dynamics model with peg-and-holes of various shapes, scales, and elasticities, the model could quickly transfer to new peg-and-holes after a small number of trials. Extensive experiments show that our dynamics model could adapt to unseen peg-and-holes with 70% fewer samples required compared to learning from scratch. Along with the learned dynamics, model predictive control and model-based reinforcement learning policies achieve over 80% insertion success rate. Our video is available at https://youtu.be/ZAqldpVZgm4.