Technical Directions
Robotics coverage relating to open artifacts.
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Neura Robotics, a German humanoid robot company, is shifting focus from hardware production to training infrastructure. Their TUM RoboGym and Neuraverse platform aim to systematize the teaching of physical tasks, enabling scalable and reusable skills across different robots and industries. The company emphasizes the importance of real-world data and physical interaction in training robots for industrial applications.
110 humanoid robots completed complex synchronized actions in 5 minutes and 43 seconds, successfully breaking the Guinness World Record. The system achieved high levels of autonomous operation, including autonomous positioning, dynamic formation, and task scheduling, marking an important step toward the practical application of humanoid robots.
ZeroDex is a zero-shot framework for long-horizon dexterous manipulation that grounds language instructions into executable 3D task plans using calibrated multi-view RGB images. It extends vision-language model (VLM) grounding from 2D to 3D by fusing multi-view data, enabling contact points, placement targets, and tool-motion anchors. The system executes complex instructions by decomposing them into atomic tasks using a small library of primitives, and outperforms zero-shot visual-prompting and constraint-based baselines. It also demonstrates robustness to intermediate failures and generalization to novel objects and tool-use tasks.
At the 2026 China International Import Expo, Faoro Robotics showcased its industrial-grade force-controlled humanoid arms (ART series), embodied AI robots (BW10-Lite), and smart workstation solutions for tasks like shoe manufacturing and box folding. The company announced it has secured nearly 1 billion yuan in C+ and D round funding from investors including Meituan Dragon Ball, along with five strategic partnerships to advance its industrial autonomy ecosystem.
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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