mlsanityData to 14 Oct 2026

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

arXiv 2503.19893 ↗First posted 25 Mar 2025cs.RO cs.CVcited by 1

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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.

Key facts. Cited by 1 paper 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

18 of 36 references.

References · 18

  1. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 775
  2. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 495
  3. 2023FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objectscited by 429
  4. 2018DeepIM: Deep Iterative Matching for 6D Pose Estimationcited by 283
  5. 2022DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Realitycited by 149
  6. 2021FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimationcited by 144
  7. 2023LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learningcited by 79
  8. 2022Theseus: A Library for Differentiable Nonlinear Optimizationcited by 65
  9. 2022Coupled Iterative Refinement for 6D Multi-Object Pose Estimationcited by 48
  10. 2022Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidancecited by 23
  11. 2022Tac2Pose: Tactile Object Pose Estimation from the First Touchcited by 15
  12. 2021DiffSDFSim: Differentiable Rigid-Body Dynamics With Implicit Shapescited by 14
6 more
  1. 2023FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedbackcited by 12
  2. 2020In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulationcited by 10
  3. 2021PatchGraph: In-hand tactile tracking with learned surface normalscited by 10
  4. 2023Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensorscited by 10
  5. 2022Tactile Pose Estimation and Policy Learning for Unknown Object Manipulationcited by 8
  6. 2024VinT-6D: A Large-Scale Object-in-hand Dataset from Vision, Touch and Proprioceptioncited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodimentscited by 4