# EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video

arXiv: https://arxiv.org/abs/2606.09243 · HTML: https://mlsanity.com/paper/2606.09243/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation](https://mlsanity.com/paper/2005.14679/) (2020)
- [HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos](https://mlsanity.com/paper/2411.19167/) (2024)
- [PressureVision: Estimating Hand Pressure from a Single RGB Image](https://mlsanity.com/paper/2203.10385/) (2022)
- [OPENTOUCH: Bringing Full-Hand Touch to Real-World Interaction](https://mlsanity.com/paper/2512.16842/) (2025)
- [PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images](https://mlsanity.com/paper/2301.02310/) (2023)
- [TouchInsight: Uncertainty-aware Rapid Touch and Text Input for Mixed Reality from Egocentric Vision](https://mlsanity.com/paper/2410.05940/) (2024)
- [Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation](https://mlsanity.com/paper/2410.23629/) (2024)
- [A Humanoid Visual-Tactile-Action Dataset for Contact-Rich Manipulation](https://mlsanity.com/paper/2510.25725/) (2025)
- [EgoPressDiff: Multimodal Video Diffusion for Egocentric UV-Domain Hand-Pressure Estimation](https://mlsanity.com/paper/2606.06872/) (2026)

## Built on by (most cited first, 4 of 4)

- [ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine](https://mlsanity.com/paper/2607.28625/) (2026)
- [Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands](https://mlsanity.com/paper/2609.15726/) (2026)
- [TouchSight: Bare-Handed Tactile Prediction from Egocentric Video via Generative Visual Augmentation](https://mlsanity.com/paper/2609.20414/) (2026)
- [From Seeing to Acting: Smart Glasses as First-Person Intelligence Platforms](https://mlsanity.com/paper/2608.24877/) (2026)

## Abstract (arXiv)

Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation, yet dense tactile sensing often relies on intrusive hardware. Existing vision-based methods predominantly rely on planar surfaces or fingertip contacts, failing to generalize to complex 3D object interactions. Therefore, we introduce EgoTactile, a benchmark pairing egocentric video with full-hand pressure supervision for diverse everyday objects, incorporating a bare-hand transfer subset to enable generalization to natural scenarios. Leveraging this benchmark, we first establish EgoPressureFormer as a discriminative baseline. Beyond this, to explicitly address the uncertainty in partial observations, we propose EgoPressureDiff, a conditional diffusion framework that adapts a large-scale pre-trained video diffusion backbone. By combining rich world knowledge priors with a Physically-Informed Feature Rectification layer to inject semantic constraints, our approach effectively infers plausible contact patterns and resolves visual-physical ambiguities. Extensive experiments demonstrate that our method achieves superior performance on the benchmark and robust transferability to in-the-wild scenarios. Our project page is available at https://egotactile.github.io/.