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 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.
Advances in computer vision and haptic technologies are converging, enabling more sophisticated and less intrusive methods for tactile sensing in robotics and virtual reality. The demand for more realistic and versatile human-robot and human-computer interaction is driving this research.
This development represents a significant step towards enabling much more capable robotic manipulation and immersive virtual reality by providing robots with a crucial sense of touch, akin to human capabilities. Better grasp pressure understanding is fundamental for fine motor control and safe interaction with diverse objects.
Current limitations in vision-based tactile sensing, which often rely on restrictive setups like planar surfaces, are overcome by this work's full-hand pressure estimation from egocentric video. This opens up new possibilities for robots and VR systems to interact with complex 3D objects more naturally and effectively.
- · Robotics companies (especially for manipulation)
- · VR/AR developers and hardware manufacturers
- · Logistics and manufacturing automation
- · AI researchers in embodiment and interactive AI
- · Companies reliant on less sophisticated or intrusive tactile sensing hardware
- · Manual labor in repetitive manipulation tasks
Robots will gain improved dexterity and ability to handle delicate or irregularly shaped objects.
This enhanced robotic manipulation will accelerate automation in areas requiring fine motor skills, from surgery to domestic assistance.
The integration of such tactile data could lead to new forms of human-robot collaboration and more human-like robotic intelligence.
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Read at arXiv cs.AI