EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video

• URL: http://arxiv.org/abs/2606.09243v1
• Date: Mon, 08 Jun 2026 09:20:03 GMT
• Title: EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video
• Abstract summary: Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation.<n>We introduce EgoTactile, a benchmark pairing egocentric video with full-hand pressure supervision for diverse everyday objects.<n>We also propose EgoPressureDiff, a conditional diffusion framework that adapts a large-scale pre-trained video diffusion backbone.
• Score: 82.13376072770363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/.

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