UniProt lookups and study design sit in one place inside ScienceBuddy. PhAI Labs released #ScienceBuddy to keep research steps visible. The trace panel aids #AIforScience reproducibility.
Learning research skills from scientific feedback is a direction that is promising, and I’m looking forward to what comes next.
Great to see my friends launch their project. Good luck @zhenfei_yin_yzf@LingYang_PU@Charles_Y_Wu ☺️
What if a dexterous robot could learn a 20+ step chemistry experiment without a single on-robot training demo?
Meet TwinDEX: a pair of co-designed, three-finger, nine-DoF dexterous manipulation interface: one wearable for data collection, one for robot deployment.
The twinned design shares identical kinematics, contact surfaces, visual appearance, and sensors across collection and deployment — keeping observations and actions aligned end to end.
Trained from scratch on only a few hundred wearable demonstrations - with zero on-robot training or intervention data - TwinDEX completed a standardized chemistry experiment involving tool switches, fine force control, and bimanual coordination.
Robot-free data showed comparable learning efficiency on the multi-task evaluation, TwinDEX delivered 5.3 times effective throughput than on-robot teleoperation.
TwinDEX demonstrates that high-quality robot-free data can fully substitute for on-robot teleoperation data on challenging dexterous tasks — removing the dependency on real-robot hardware that has been the central bottleneck to scaling dexterous manipulation data.
This was the proof-it phase. Now comes scale: what emerges at tens of thousands, or millions, of episodes?
Watch the demo and read the technical blog: https://t.co/PgyFkrJdXG
#TwinDEX #Robotics #EmbodiedAI #DexterousManipulation