# Digitizing Touch with an Artificial Multimodal Fingertip

arXiv: https://arxiv.org/abs/2411.02479 · HTML: https://mlsanity.com/paper/2411.02479/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 30 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Built on by (most cited first, 20 of 20)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
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- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
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- [ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learning](https://mlsanity.com/paper/2501.14622/) (2025)
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- [DexSkin: High-Coverage Conformable Robotic Skin for Learning Contact-Rich Manipulation](https://mlsanity.com/paper/2509.18830/) (2025)
- [DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter](https://mlsanity.com/paper/2602.05513/) (2026)
- [ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation](https://mlsanity.com/paper/2606.13877/) (2026)
- [ViTaSCOPE: Visuo-tactile Implicit Representation for In-hand Pose and Extrinsic Contact Estimation](https://mlsanity.com/paper/2506.12239/) (2025)
- [Learning Gentle Grasping Using Vision, Sound, and Touch](https://mlsanity.com/paper/2503.07926/) (2025)
- [VTouch++: A Multimodal Dataset with Vision-Based Tactile Enhancement for Bimanual Manipulation](https://mlsanity.com/paper/2604.20444/) (2026)
- [Enhance Vision-based Tactile Sensors via Dynamic Illumination and Image Fusion](https://mlsanity.com/paper/2504.00017/) (2025)
- [The Role of Touch: Towards Optimal Tactile Sensing Distribution in Anthropomorphic Hands for Dexterous In-Hand Manipulation](https://mlsanity.com/paper/2509.14984/) (2025)
- [Ergodic Control and Controlled Diffusion for Robot Learning: Review and Tutorial](https://mlsanity.com/paper/2609.13295/) (2026)
- [Calibration-Free Surface Normals Estimation in Vision-Based Tactile Sensing using Universal Photometric Stereo](https://mlsanity.com/paper/2609.31754/) (2026)
- [ROMA: LLM System for Real-World Object-Centric Multi-Sensory Active Perception](https://mlsanity.com/paper/2610.06955/) (2026)
- [PhysTacGen: Physics-Aware Visual-Tactile Sensor Image Generation](https://mlsanity.com/paper/2610.08068/) (2026)
- [Temporal Visuo-Tactile Learning for Dexterous Grasp Stability](https://mlsanity.com/paper/2610.10283/) (2026)

## Abstract (arXiv)

Touch is a crucial sensing modality that provides rich information about object properties and interactions with the physical environment. Humans and robots both benefit from using touch to perceive and interact with the surrounding environment (Johansson and Flanagan, 2009; Li et al., 2020; Calandra et al., 2017). However, no existing systems provide rich, multi-modal digital touch-sensing capabilities through a hemispherical compliant embodiment. Here, we describe several conceptual and technological innovations to improve the digitization of touch. These advances are embodied in an artificial finger-shaped sensor with advanced sensing capabilities. Significantly, this fingertip contains high-resolution sensors (~8.3 million taxels) that respond to omnidirectional touch, capture multi-modal signals, and use on-device artificial intelligence to process the data in real time. Evaluations show that the artificial fingertip can resolve spatial features as small as 7 um, sense normal and shear forces with a resolution of 1.01 mN and 1.27 mN, respectively, perceive vibrations up to 10 kHz, sense heat, and even sense odor. Furthermore, it embeds an on-device AI neural network accelerator that acts as a peripheral nervous system on a robot and mimics the reflex arc found in humans. These results demonstrate the possibility of digitizing touch with superhuman performance. The implications are profound, and we anticipate potential applications in robotics (industrial, medical, agricultural, and consumer-level), virtual reality and telepresence, prosthetics, and e-commerce. Toward digitizing touch at scale, we open-source a modular platform to facilitate future research on the nature of touch.