# Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation

arXiv: https://arxiv.org/abs/2203.10685 · HTML: https://mlsanity.com/paper/2203.10685/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2022.

## Built on by (most cited first, 8 of 8)

- [Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play](https://mlsanity.com/paper/2303.12076/) (2023)
- [See to Touch: Learning Tactile Dexterity through Visual Incentives](https://mlsanity.com/paper/2309.12300/) (2023)
- [AcTExplore: Active Tactile Exploration of Unknown Objects](https://mlsanity.com/paper/2310.08745/) (2023)
- [Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins](https://mlsanity.com/paper/2410.17246/) (2024)
- [Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors](https://mlsanity.com/paper/2301.13667/) (2023)
- [Tactile-Filter: Interactive Tactile Perception for Part Mating](https://mlsanity.com/paper/2303.06034/) (2023)
- [Visuo-Tactile Object Pose Estimation for a Multi-Finger Robot Hand with Low-Resolution In-Hand Tactile Sensing](https://mlsanity.com/paper/2503.19893/) (2025)
- [BEACON: Belief-Enabled Adaptive CONtrol for Imitation Learning under Uncertainty](https://mlsanity.com/paper/2609.22730/) (2026)

## Abstract (arXiv)

Object pose estimation methods allow finding locations of objects in unstructured environments. This is a highly desired skill for autonomous robot manipulation as robots need to estimate the precise poses of the objects in order to manipulate them. In this paper, we investigate the problems of tactile pose estimation and manipulation for category-level objects. Our proposed method uses a Bayes filter with a learned tactile observation model and a deterministic motion model. Later, we train policies using deep reinforcement learning where the agents use the belief estimation from the Bayes filter. Our models are trained in simulation and transferred to the real world. We analyze the reliability and the performance of our framework through a series of simulated and real-world experiments and compare our method to the baseline work. Our results show that the learned tactile observation model can localize the pose of novel objects at 2-mm and 1-degree resolution for position and orientation, respectively. Furthermore, we experiment on a bottle opening task where the gripper needs to reach the desired grasp state.