From World Robot Conference 2026 in Beijing: @BrainCo_Tech demonstrated bimanual robot teleoperation using @ManusMeta gloves, mapping the operator’s hand movements to the robot in real time for coordinated dexterous manipulation. #WRC2026# #robotics# #teleoperation# #DexterousManipulation# #PhysicalAI# #humanoids#

From World Robot Conference 2026 in Beijing: Xynova demonstrated dexterous hand teleoperation using @ManusMeta gloves, translating the operator’s hand movements into real-time robotic control with high-fidelity hand motion data. #WRC2026# #robotics# #teleoperation# #DexterousManipulation# #PhysicalAI#

Excited to see WUJI as part of the @NVIDIA Isaac Teleop ecosystem. Wuji Glove captures human hand motion. Wuji Hand brings it into dexterous robot execution. Looking forward to exploring what comes next with NVIDIA and the broader Physical AI ecosystem. #WujiTech# #NVIDIA# #WujiHand# #WujiHand2# #DexterousHand# #Robotics# #PhysicalAI# #DexterousManipulation#

Human motion. Human touch. A new chapter begins. Two next-generation products from debuting at CoRL 2026: 🔹 DAS Ego 2.0 — A first-person multimodal data collection system with hand-tracking precision within 4 mm. 🔹 DAS Dex 2.0 — A high-fidelity human dexterity data glove with e-skin tactile sensing. From whole-body motion to tactile sensing, perfectly in sync. Come experience it live. See you at Booth #200#! 📅 November 9–11, 2026 📍 110 E 2nd St, Austin, TX 78701, USA #CoRL2026# #GenRobotAI# #Robotics# #EgoData# #WholeBodyManipulation# #DexterousManipulation# #EmbodiedAI# #RobotLearning#

Wuji Tech × Alibaba DAMO Academy RynnBrain 1.1 is tested with Wuji Hand on real-robot dexterous manipulation tasks. The demo spans grasping spatulas, flipping steak, plating, serving dishes, and pouring wine. A good testbed for connecting embodied perception, contact reasoning, and reliable execution in continuous manipulation. #WujiTech# #WujiHand# #RynnBrain# #EmbodiedAI# #Robotics# #DexterousManipulation#

During ICRA 2026, we hosted a salon bringing together researchers, founders, and industry partners. Our founder, Yunzhe Pan, shared the story behind Wuji Tech. The conversation moved beyond just our product, extending to data, training systems, Sim-to-Real, and commercialization in embodied AI. While the questions varied, they all focus on one truth: what it takes to make embodied intelligence work in the real world. Thank you to everyone who joined and shared their perspectives. #ICRA2026# #Robotics# #EmbodiedAI# #WujiTech# #WujiHand# #DexterousaHand# #DexterousManipulation#

Wuji Hand 2 Makes Its Global Debut at ICRA 2026 Next-generation dexterous hand with 20 DOF, backdrivable actuation, and improved thermal design for sustained operation. Built for real-world dexterous manipulation. #WujiTech# #WujiHand# #ICRA2026# #DexterousHand# #Robotics# #EmbodiedAI#

More than 150 researchers and academics from leading universities around the world joined AGIBOT’s Academic Night at IROS 2026 to discuss one big question: what will it take to move embodied intelligence forward? The conversations went well beyond demos. From robot learning and VLA to dexterous manipulation and control, researchers exchanged views on the challenges they are tackling today — and what they need from the next generation of robotic platforms. AGIBOT also showcased A3 Ultra, X2, OmniHand, and D1 EDU, while giving the academic community a closer look at our research platforms, development tools and some of the technical challenges we are working on. One takeaway stood out: progress toward general-purpose humanoids will require closer collaboration between academia and industry. We look forward to building more with universities and labs around the world. @AGIBOT_US #IROS2026# #AGIBOT# #Robotics# #EmbodiedAI# #Research#

Li Auto’s Foundation Model team has released MachEmbodied-Dex-1.0, a unified World Action Tactile Model that jointly learns future video, tactile states and robot actions. ME-Dex-1.0 elevates tactile sensing from an auxiliary condition to a future world state that the model must predict. It uses a unified tactile representation to align heterogeneous sensors, while a three-expert architecture connects predicted visual changes and contact dynamics to action generation. An Agentic Tactile Data Engine expands paired visual-tactile-action data through simulation. Results: • RoboTwin: 78.9% Clean-only success rate, +7.6 points vs. the strongest baseline • DexJoCo: #1# on 7 of 11 dexterous manipulation tasks • ManiFeel: plug-insertion success rate jumped from 58% → 88%

Reward AI is based in the SF Bay Area. The founders are two of Stanford's standout robot-learning researchers: Zipeng Fu (Cofounder, CEO), advised by Chelsea Finn, ex-Google DeepMind. Lead author on Mobile ALOHA (the viral $32k robot that cooks and cleans) and HumanPlus (humanoids learning by shadowing humans). Chen Wang (Cofounder, CTO), advised by Fei-Fei Li and Karen Liu. Lead author on DexCap, the portable dexterous-manipulation capture system OM-1's wearable hand is built on.