WAIC现场，这家公司让一群不同的机器人共用一个大脑

TL;DR - At WAIC, Mech-Mind demonstrated a shared “eye-brain-hand” stack that powers humanoids, mobile robots, and industrial arms across retail and manufacturing tasks. The approach matters because it prioritizes reusable intelligence, deployment scale, and industrial reliability over any single robot form.

• Mech-GPT interprets instructions and plans tasks using multimodal inputs, while 3D vision, world-action models, motion planning, and dexterous hands execute them.
• Demonstrations covered object sorting, transparent-object grasping, shelf picking, coordinated assembly, and flexible cable insertion.
• Reported industrial performance included sub-2.4-second small-part handling and submillimeter assembly precision.
• Mech-Mind says its standardized products exceed 27,000 deployments across nearly 50 countries and multiple industries.

TL;DR - Sudo Technology showcased a full-stack robotics platform at WAIC, demonstrating generalized manipulation and a CATL battery-production deployment. The company’s strategy combines multimodal data, simulation-to-real transfer, world models, reinforcement learning, and integrated hardware to make reusable robot skills reliable in real environments.

• Its 10+ skills include grasping, insertion, assembly, deformable-object handling, mobile manipulation, and multi-robot coordination.
• The platform fuses simulation, teleoperation, UMI, egocentric, and internet-video data rather than relying solely on synthetic data.
• Sudo integrates physical-dynamics prediction with reinforcement learning and co-designs robot hardware, control, simulation, models, and evaluation.
• A CATL production-line POC reportedly achieved over 99.5% success for industrial pick-and-place, supporting a “one robot, multiple tasks” deployment model.

TL;DR - At WAIC 2026, Openmind debuted a hardware-agnostic robotics stack positioned as the “Android” of robotics. It aims to reduce embodied-AI development costs by unifying data collection, model training, low-code application development, and cross-robot deployment.

• Openmind OS supports industrial arms, mobile manipulators, and humanoids across x86/ARM, with ROS2 and third-party algorithm compatibility.
• The stack combines HALO wearable data capture, the Dayan data platform, Modou low-code IDE, and the HumanGPT world model.
• HumanGPT learns transferable skills from multimodal human-operation data, reducing reliance on costly robot-collected demonstrations.
• Openmind demonstrated fine manipulation, autonomous lunar-construction tasks, and no-code adaptive spray painting as industrial validation scenarios.

TL;DR - MARHE launched its third-generation HummingDrive frameless torque motors at WAIC, claiming the world’s smallest series. The compact, high-torque motors target dexterous humanoid hands, surgical robots, exoskeletons, and other space-constrained systems.

• Models span 9.9–20 mm in outer diameter, with the smallest weighing 3.5 g and peak torque reaching 12.33 mNm.
• The motors combine 1.6 T rare-earth permanent magnets with a Halbach topology to increase air-gap magnetic flux and torque density.
• Manufacturing requires precision forming, multidirectional magnetization, micron-scale automated assembly, and ultrathin carbon-fiber rotor reinforcement.
• MARHE developed the series over two years in collaboration with humanoid-robot companies and research teams.

TL;DR - A WAIC forum highlighted data—not model architecture—as the main bottleneck to scalable embodied AI. Industry leaders are exploring real-robot, egocentric human, video, wearable, and deployment data to build generalizable physical intelligence.

• Key barriers are scarce interaction data, missing cross-task/robot representations, and costly real-world feedback loops.
• Physical Intelligence and Dyna emphasized learning from failures and recycling deployment data into training; Dyna reported 99.4% success across 850+ napkin-folding trials after RL post-training.
• Speakers advocated combining robot, human egocentric, UMI, video, and internet data to improve diversity and transfer across embodiments.
• Estimates for an embodied-AI “ChatGPT moment” ranged from under two to five years, largely depending on progress in data collection and closed-loop learning.