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Robots learn manipulation and grasp pressure from egocentric video

By PulseAugur Editorial · [6 sources] ·

Researchers have developed new frameworks for robots to learn dexterous manipulation and grasp pressure from egocentric video demonstrations. EgoAERO enables robots to learn manipulation skills from single human demonstrations without needing object assets, converting them into robot policies. EgoTactile and EgoPressDiff focus on estimating grasp pressure from egocentric video, using diffusion models and multimodal conditioning to achieve state-of-the-art results in realistic scenarios. AI

IMPACT Enables more sophisticated robotic manipulation and human-computer interaction by learning complex skills from visual data.

RANK_REASON Multiple research papers introducing new frameworks and datasets for AI-driven manipulation and grasp estimation.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

Robots learn manipulation and grasp pressure from egocentric video

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COVERAGE [6]

  1. arXiv cs.AI TIER_1 English(EN) · Yichen Niu, Haoran Lv, Xinrui Zhang, Xueyao Wan, Shiyu Gao, Ying Ai, Hui Xu, Yongqi Hu, Hengyi Zhang, Yang Xie, Zhaxizhuoma, Yue Zhao, Zhenshan Bing, Yan Ding, Jianxing Liu ·

    EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets

    arXiv:2606.08057v1 Announce Type: cross Abstract: Egocentric RGB-D videos offer a natural source of human dexterous manipulation demonstrations, but existing data is difficult to use for robot learning because object pose, geometry, and contact information are often missing or re…

  2. arXiv cs.AI TIER_1 English(EN) · Yuan Zeng, Yujia Shi, Tiao Tan, Xingting Li, Yaqi Qin, Zongqing Lu, Wenming Yang, Jing-Hao Xue, Qingmin Liao ·

    EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video

    arXiv:2606.09243v1 Announce Type: cross 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 pla…

  3. arXiv cs.AI TIER_1 English(EN) · Yuan Zeng, Zilue Gao, Yujia Shi, Zongqing Lu, Wenming Yang, QingMin Liao ·

    EgoPressDiff: Multimodal Video Diffusion for Egocentric UV-Domain Hand-Pressure Estimation

    arXiv:2606.06872v1 Announce Type: cross Abstract: Estimating hand-surface contact pressure from an egocentric view is crucial for AR/VR devices, robotic imitation, and ergonomic analysis. Existing methods often discretize pressure signal and process frames independently, leading …

  4. arXiv cs.CV TIER_1 English(EN) · Zhifan Zhu, Yifei Huang, Yoichi Sato, Dima Damen ·

    The N-Body Problem: Parallel Execution from Single-Person Egocentric Video

    arXiv:2512.11393v2 Announce Type: replace Abstract: Humans can intuitively parallelise complex activities, but can a model predict this from observing a single person? Given one egocentric video, we introduce the N-Body Problem: predicting how N individuals, can hypothetically pe…

  5. arXiv cs.CV TIER_1 English(EN) · Qingmin Liao ·

    EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video

    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 gen…

  6. arXiv cs.CV TIER_1 English(EN) · QingMin Liao ·

    EgoPressDiff: Multimodal Video Diffusion for Egocentric UV-Domain Hand-Pressure Estimation

    Estimating hand-surface contact pressure from an egocentric view is crucial for AR/VR devices, robotic imitation, and ergonomic analysis. Existing methods often discretize pressure signal and process frames independently, leading to quantization errors and temporal inconsistencie…

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