# Active Extrinsic Contact Sensing: Application to General Peg-in-Hole Insertion

arXiv: https://arxiv.org/abs/2110.03555 · HTML: https://mlsanity.com/paper/2110.03555/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2021.

## Built on by (most cited first, 19 of 19)

- [MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation](https://mlsanity.com/paper/2310.16917/) (2023)
- [The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning](https://mlsanity.com/paper/2311.00924/) (2023)
- [AnySkin: Plug-and-play Skin Sensing for Robotic Touch](https://mlsanity.com/paper/2409.08276/) (2024)
- [Tac2Pose: Tactile Object Pose Estimation from the First Touch](https://mlsanity.com/paper/2204.11701/) (2022)
- [Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins](https://mlsanity.com/paper/2410.17246/) (2024)
- [Built Different: Tactile Perception to Overcome Cross-Embodiment Capability Differences in Collaborative Manipulation](https://mlsanity.com/paper/2409.14896/) (2024)
- [LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping](https://mlsanity.com/paper/2403.04934/) (2024)
- [DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation](https://mlsanity.com/paper/2509.18830/) (2025)
- [Integrated Object Deformation and Contact Patch Estimation from Visuo-Tactile Feedback](https://mlsanity.com/paper/2305.14470/) (2023)
- [Cross-Sensor Touch Generation](https://mlsanity.com/paper/2510.09817/) (2025)
- [Tactile Estimation of Extrinsic Contact Patch for Stable Placement](https://mlsanity.com/paper/2309.14552/) (2023)
- [Compact Multi-Object Placement Using Adjacency-Aware Reinforcement Learning](https://mlsanity.com/paper/2404.10632/) (2024)
- [OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies](https://mlsanity.com/paper/2607.03723/) (2026)
- [Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks](https://mlsanity.com/paper/2409.16208/) (2024)
- [Object Pose Estimation through Dexterous Touch](https://mlsanity.com/paper/2509.13591/) (2025)
- [TranTac: Leveraging Transient Tactile Signals for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2509.16550/) (2025)
- [SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy](https://mlsanity.com/paper/2605.12247/) (2026)
- [Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Contact-Rich Assembly](https://mlsanity.com/paper/2609.07534/) (2026)
- [Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation](https://mlsanity.com/paper/2610.03333/) (2026)

## Abstract (arXiv)

We propose a method that actively estimates contact location between a grasped rigid object and its environment and uses this as input to a peg-in-hole insertion policy. An estimation model and an active tactile feedback controller work collaboratively to estimate the external contacts accurately. The controller helps the estimation model get a better estimate by regulating a consistent contact mode. The better estimation makes it easier for the controller to regulate the contact. We then train an object-agnostic insertion policy that learns to use the series of contact estimates to guide the insertion of an unseen peg into a hole. In contrast with previous works that learn a policy directly from tactile signals, since this policy is in contact configuration space, it can be learned directly in simulation. Lastly, we demonstrate and evaluate the active extrinsic contact line estimation and the trained insertion policy together in a real experiment. We show that the proposed method inserts various-shaped test objects with higher success rates and fewer insertion attempts than previous work with end-to-end approaches. See supplementary video and results at https://sites.google.com/view/active-extrinsic-contact.