Technical Directions
Robotics coverage relating to open artifacts.
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This paper presents OmniDex, a benchmark and model for dexterous hand grasping in cluttered scenes. It addresses data scarcity through a scalable seed-and-filter strategy, generating over 2.6 million scenes and 0.4B grasp ground truths. The model combines Soft Winner-Takes-All learning with physical constraints to achieve robust grasping without post-optimization latency, showing strong generalization across diverse scenarios.
Carnegie Mellon University researchers have developed a robotic fingertip that enables robots to handle delicate tasks, such as picking up fragile or tiny objects like pills and potato chips. The fingertip, which attaches to the DeltaHands device, uses a strain gauge and contact microphone to measure pressure and capture microvibrations, allowing the robot to sense and manipulate objects with greater precision. The low-cost, 3D-printable design offers versatility for a range of tasks, from counting paper cups to identifying material compositions through vibrations. The work was presented at the 2026 Intelligent Robots and Systems Conference.
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