# A Survey of Robot Manipulation in Contact

arXiv: https://arxiv.org/abs/2112.01942 · HTML: https://mlsanity.com/paper/2112.01942/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2021.

## Built on by (most cited first, 28 of 28)

- [NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation](https://mlsanity.com/paper/2402.15852/) (2024)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [Touch begins where vision ends: Generalizable policies for contact-rich manipulation](https://mlsanity.com/paper/2506.13762/) (2025)
- [Visuo-Haptic Object Perception for Robots: An Overview](https://mlsanity.com/paper/2203.11544/) (2022)
- [GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2404.06645/) (2024)
- [Is Single-View Mesh Reconstruction Ready for Robotics?](https://mlsanity.com/paper/2505.17966/) (2025)
- [Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks](https://mlsanity.com/paper/2503.21989/) (2025)
- [Contact Map Transfer with Conditional Diffusion Model for Generalizable Dexterous Grasp Generation](https://mlsanity.com/paper/2511.01276/) (2025)
- [Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning](https://mlsanity.com/paper/2511.14427/) (2025)
- [FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation](https://mlsanity.com/paper/2503.13418/) (2025)
- [Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation](https://mlsanity.com/paper/2609.01596/) (2026)
- [Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based Curriculum](https://mlsanity.com/paper/2502.12599/) (2025)
- [Learning Rock Pushability on Rough Planetary Terrain](https://mlsanity.com/paper/2505.09833/) (2025)
- [SCAR: Self-Supervised Continuous Action Representation Learning](https://mlsanity.com/paper/2605.16412/) (2026)
- [PAC-ACT: Post-training Actor-Critic for Action Chunking Transformers](https://mlsanity.com/paper/2607.09590/) (2026)
- [CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation](https://mlsanity.com/paper/2609.05832/) (2026)
- [Constraint-Grounded Reinforcement Learning for Variable Impedance Control in Contact-Rich Robotic Insertion](https://mlsanity.com/paper/2609.13516/) (2026)
- [Augmentation for Learning From Demonstration with Environmental Constraints](https://mlsanity.com/paper/2210.07015/) (2022)
- [A Central Motor System Inspired Pre-training Reinforcement Learning for Robotic Control](https://mlsanity.com/paper/2311.07822/) (2023)
- [COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty](https://mlsanity.com/paper/2403.14488/) (2024)
- [KineDepth: Utilizing Robot Kinematics for Online Metric Depth Estimation](https://mlsanity.com/paper/2409.19490/) (2024)
- [Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images](https://mlsanity.com/paper/2502.00275/) (2025)
- [Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist](https://mlsanity.com/paper/2601.19275/) (2026)
- [Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty](https://mlsanity.com/paper/2604.19677/) (2026)
- [Spacetime Optimal-Transport Attention for Visuo-Haptic Imitation Learning of Contact-Rich Manipulation](https://mlsanity.com/paper/2605.20433/) (2026)
- [WA-SpecDec: World-Aware Speculative Decoding for Vision-Language-Action Models](https://mlsanity.com/paper/2608.08725/) (2026)
- [Hybrid Residual Reinforcement Learning for Contact-Rich Robotic Book Insertion](https://mlsanity.com/paper/2609.19962/) (2026)

## Abstract (arXiv)

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of 1) performing tasks that always require contact and 2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks.