WUJI 开源 20 关节机械手转笔训练方案
WUJI 把转笔这种高难度动作做成了开源方案，20 关节手加数据手套，代码模型全放出来了，有 NVIDIA GPU 就能复现。
WUJI 在 IROS 2026 展示了 WUJI Hand 2，这只机械手有 20 个关节、每指 4 个且各由独立电机驱动，控制频率达 1,000 Hz。团队先在 MuJoCo 物理模拟器里用 mjlab 工具包和 PPO 算法训练转笔策略，同时运行 4,096 个虚拟手副本。真机上策略以每秒 50 次向 20 个关节发送目标，工业相机追踪 250mm 3D 打印笔两端的标记。配套数据手套用 5 个电磁传感器以每秒 120 次采集手指位姿，延迟约 10ms，掌心 526 点压力网格记录握力。代码、动作片段、训练模型和可打印笔文件均以 Apache 2.0 许可开源。
Another beautiful robotic hand.
WUJI trained its 20-joint robot hand, WUJI Hand 2, to spin a pen inside a physics simulator, then ran that same trained policy on the real hand.
It showed this around IROS 2026 (the International Conference on Intelligent Robots and Systems), together with a sensor glove that lets a person control the hand live.
Pen spinning is a hard test for a robot hand, because the pen keeps rolling, sliding and passing between fingers, and one finger moving a bit late drops it. @wuji_global’s Hand 2 has 20 joints, four per finger, each independently driven by its own motor, and supports a 1,000 Hz control rate.
Training used mjlab, a robot-learning toolkit built on the MuJoCo physics simulator, running 4,096 virtual copies of the hand at the same time with PPO (Proximal Policy Optimization), a reinforcement learning method where the AI improves by trial and error and gets rewarded for good moves.
The AI learns to follow 40 recorded pen motions in 5 groups, from a simple turn around 1 axis up to continuous spins across several axes, and it keeps correcting its fingers whenever the pen drifts from the plan.
On the real hand, the policy sends targets to all 20 joints 50 times a second, while an industrial camera tracks printed markers on both ends of a 250mm 3D-printed pen plus a tag on the wrist, so the AI always knows where the pen is.
The glove covers the other way of teaching a robot: 5 electromagnetic sensors at the fingertips track each finger's position and angle 120 times a second with about 10ms delay or less, and software turns that into joint angles the robot hand copies.
A 526-point pressure grid on the glove's palm also records how hard the person grips, which makes it useful for collecting training data for robots that learn by copying humans.
WUJI released the code, motion clips, trained models and printable pen files under the open Apache 2.0 license, so a lab with a WUJI Hand 2, a camera and an NVIDIA GPU can repeat the whole setup.
