# Tactile Dexterity: Manipulation Primitives with Tactile Feedback

arXiv: https://arxiv.org/abs/2002.03236 · HTML: https://mlsanity.com/paper/2002.03236/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2020.

## Built on by (most cited first, 23 of 23)

- [Rotating without Seeing: Towards In-hand Dexterity through Touch](https://mlsanity.com/paper/2303.10880/) (2023)
- [GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger](https://mlsanity.com/paper/2106.08851/) (2021)
- [Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2401.12024/) (2024)
- [The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning](https://mlsanity.com/paper/2311.00924/) (2023)
- [Zero-shot sim-to-real transfer of tactile control policies for aggressive swing-up manipulation](https://mlsanity.com/paper/2101.02680/) (2021)
- [Touch begins where vision ends: Generalizable policies for contact-rich manipulation](https://mlsanity.com/paper/2506.13762/) (2025)
- [FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors](https://mlsanity.com/paper/2410.02048/) (2024)
- [Robust Pivoting: Exploiting Frictional Stability Using Bilevel Optimization](https://mlsanity.com/paper/2203.11412/) (2022)
- [Action Conditioned Tactile Prediction: case study on slip prediction](https://mlsanity.com/paper/2205.09430/) (2022)
- [M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation](https://mlsanity.com/paper/2401.17032/) (2024)
- [Sensor-Invariant Tactile Representation](https://mlsanity.com/paper/2502.19638/) (2025)
- [Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation](https://mlsanity.com/paper/2504.12908/) (2025)
- [Tactile-Filter: Interactive Tactile Perception for Part Mating](https://mlsanity.com/paper/2303.06034/) (2023)
- [LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping](https://mlsanity.com/paper/2403.04934/) (2024)
- [Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints](https://mlsanity.com/paper/2203.15155/) (2022)
- [Robust Pivoting Manipulation using Contact Implicit Bilevel Optimization](https://mlsanity.com/paper/2303.08965/) (2023)
- [VTAO-BiManip: Masked Visual-Tactile-Action Pre-training with Object Understanding for Bimanual Dexterous Manipulation](https://mlsanity.com/paper/2501.03606/) (2025)
- [TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulation](https://mlsanity.com/paper/2609.05266/) (2026)
- [Behavioral Learning of Dish Rinsing and Scrubbing based on Interruptive Direct Teaching Considering Assistance Rate](https://mlsanity.com/paper/2408.09360/) (2024)
- [Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations](https://mlsanity.com/paper/2508.01082/) (2025)
- [Unified Visual-Tactile-Action Modeling from Human Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2609.34182/) (2026)
- [Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation](https://mlsanity.com/paper/2610.03333/) (2026)
- [Robot Learning with Visual Predicted Force](https://mlsanity.com/paper/2610.04741/) (2026)

## Abstract (arXiv)

This paper develops closed-loop tactile controllers for dexterous robotic manipulation with a dual-palm robotic system. Tactile dexterity is an approach to dexterous manipulation that plans for robot/object interactions that render interpretable tactile information for control. We divide the role of tactile control into two goals: 1) control the contact state between the end-effector and the object (contact/no-contact, stick/slip) by regulating the stability of planned contact configurations and monitoring undesired slip events; and 2) control the object state by tactile-based tracking and iterative replanning of the object and robot trajectories. Key to this formulation is the decomposition of manipulation plans into sequences of manipulation primitives with simple mechanics and efficient planners. We consider the scenario of manipulating an object from an initial pose to a target pose on a flat surface while correcting for external perturbations and uncertainty in the initial pose of the object. We experimentally validate the approach with an ABB YuMi dual-arm robot and demonstrate the ability of the tactile controller to react to external perturbations.