# Visuo-Tactile Object Pose Estimation for a Multi-Finger Robot Hand with Low-Resolution In-Hand Tactile Sensing

arXiv: https://arxiv.org/abs/2503.19893 · HTML: https://mlsanity.com/paper/2503.19893/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 18 of 18)

- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [DeepIM: Deep Iterative Matching for 6D Pose Estimation](https://mlsanity.com/paper/1804.00175/) (2018)
- [DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality](https://mlsanity.com/paper/2210.13702/) (2022)
- [FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation](https://mlsanity.com/paper/2103.02242/) (2021)
- [LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning](https://mlsanity.com/paper/2309.06440/) (2023)
- [Theseus: A Library for Differentiable Nonlinear Optimization](https://mlsanity.com/paper/2207.09442/) (2022)
- [Coupled Iterative Refinement for 6D Multi-Object Pose Estimation](https://mlsanity.com/paper/2204.12516/) (2022)
- [Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance](https://mlsanity.com/paper/2212.09902/) (2022)
- [Tac2Pose: Tactile Object Pose Estimation from the First Touch](https://mlsanity.com/paper/2204.11701/) (2022)
- [DiffSDFSim: Differentiable Rigid-Body Dynamics With Implicit Shapes](https://mlsanity.com/paper/2111.15318/) (2021)
- [FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback](https://mlsanity.com/paper/2303.07997/) (2023)
- [In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation](https://mlsanity.com/paper/2002.12160/) (2020)
- [PatchGraph: In-hand tactile tracking with learned surface normals](https://mlsanity.com/paper/2111.07524/) (2021)
- [Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors](https://mlsanity.com/paper/2301.13667/) (2023)
- [Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation](https://mlsanity.com/paper/2203.10685/) (2022)
- [VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and Proprioception](https://mlsanity.com/paper/2501.00510/) (2024)

## Built on by (most cited first, 1 of 1)

- [HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments](https://mlsanity.com/paper/2604.14944/) (2026)

## Abstract (arXiv)

Accurate 3D pose estimation of grasped objects is an important prerequisite for robots to perform assembly or in-hand manipulation tasks, but object occlusion by the robot's own hand greatly increases the difficulty of this perceptual task. Here, we propose that combining visual information and proprioception with binary, low-resolution tactile contact measurements from across the interior surface of an articulated robotic hand can mitigate this issue. The visuo-tactile object-pose-estimation problem is formulated probabilistically in a factor graph. The pose of the object is optimized to align with the three kinds of measurements using a robust cost function to reduce the influence of visual or tactile outlier readings. The advantages of the proposed approach are first demonstrated in simulation: a custom 15-DoF robot hand with one binary tactile sensor per link grasps 17 YCB objects while observed by an RGB-D camera. This low-resolution in-hand tactile sensing significantly improves object-pose estimates under high occlusion and also high visual noise. We also show these benefits through grasping tests with a preliminary real version of our tactile hand, obtaining reasonable visuo-tactile estimates of object pose at approximately 13.3 Hz on average.