arXiv:2606.11767v1 Announce Type: cross Abstract: Blind grasping with a dexterous hand is a crucial manipulation capability. Nevertheless, learning such tactile-only policies for real robots remains challenging due to the tactile sim-to-real gap and the limited expressiveness of sparse tactile signals. To bridge this gap, we propose a framework for tactile-only blind grasping that is deployable on a physical multi-fingered robotic hand. Our approach combines three key components. First, we introduce a Real2Sim tactile calibration pipeline that constructs a contact-calibrated digital-twin simul
The proliferation of advanced robotics and AI research continually pushes the boundaries of autonomous manipulation, with tactile sensing being a critical, yet challenging, frontier for dexterous handling.
Achieving advanced tactile-driven grasping enables robots to perform complex tasks in unstructured environments, crucial for automation in manufacturing, logistics, and service industries.
The proposed 'Real2Sim2Real' framework and tactile calibration pipeline significantly reduce the sim-to-real gap for tactile policies, making dexterous robot hands more practical and robust for real-world applications.
- · Robotics manufacturers
- · Logistics and e-commerce
- · AI research and development
- · Manufacturing sector
- · Manual labor in repetitive manipulation tasks
Increased adoption of multi-fingered robotic hands in industrial settings due to improved reliability and capability.
Demand for more sophisticated and robust tactile sensors will grow, driving innovation in sensor technology.
Enhanced robotic dexterity could enable new forms of automated assembly and personalized manufacturing, reducing costs and increasing efficiency across various industries.
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Read at arXiv cs.AI