# Compare Contact Model-based Control and Contact Model-free Learning: A Survey of Robotic Peg-in-hole Assembly Strategies

arXiv: https://arxiv.org/abs/1904.05240 · HTML: https://mlsanity.com/paper/1904.05240/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering](https://mlsanity.com/paper/2210.15185/) (2022)
- [Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks](https://mlsanity.com/paper/2106.14070/) (2021)
- [Chain-of-Thought Predictive Control](https://mlsanity.com/paper/2304.00776/) (2023)
- [InsertionNet -- A Scalable Solution for Insertion](https://mlsanity.com/paper/2104.14223/) (2021)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input](https://mlsanity.com/paper/2203.01153/) (2022)
- [Symmetry-aware Reinforcement Learning for Robotic Assembly under Partial Observability with a Soft Wrist](https://mlsanity.com/paper/2402.18002/) (2024)
- [Learning Reward for Physical Skills using Large Language Model](https://mlsanity.com/paper/2310.14092/) (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)
- [Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks](https://mlsanity.com/paper/2409.16208/) (2024)
- [Zero-Shot Peg Insertion: Identifying Mating Holes and Estimating SE(2) Poses with Vision-Language Models](https://mlsanity.com/paper/2503.06026/) (2025)
- [Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty](https://mlsanity.com/paper/2604.19677/) (2026)
- [Generalizable Robotic Insertion with World Models](https://mlsanity.com/paper/2609.28258/) (2026)

## Abstract (arXiv)

In this paper, we present an overview of robotic peg-in-hole assembly and analyze two main strategies: contact model-based and contact model-free strategies. More specifically, we first introduce the contact model control approaches, including contact state recognition and compliant control two steps. Additionally, we focus on a comprehensive analysis of the whole robotic assembly system. Second, without the contact state recognition process, we decompose the contact model-free learning algorithms into two main subfields: learning from demonstrations and learning from environments (mainly based on reinforcement learning). For each subfield, we survey the landmark studies and ongoing research to compare the different categories. We hope to strengthen the relation between these two research communities by revealing the underlying links. Ultimately, the remaining challenges and open questions in the field of robotic peg-in-hole assembly community is discussed. The promising directions and potential future work are also considered.