UMI robotics dataset scales from 5 to 90 operators, 1M+ tasks in 8 weeks
HildeKuehne · x · 2026-10-08
A robotics team detailed scaling their international UMI data collection from 5 to 90 operators in 8 weeks, building a dataset of 1M+ unique tasks and capturing 45k+ new tasks daily. World models need far more task diversity than imitation learning, they explain. Google DeepMind's Lucas Beyer notes coding model progress is helping accelerate physical-world robotics.
More from Embodied
- End effector design rabbit hole: why 'grippers vs hands' is a false binary, from a practitioner — _Stocko_ · 2026-10-08
- RLHND turns video diffusion models into physically grounded hand trackers for robot learning — RLWRLD · 2026-10-08
- NEEDLEwork stitches suboptimal robot demos into better training data — SongShuran · 2026-10-08
- Stanford's MobileVISTA fixes mobile manipulation's pose brittleness with generative data augmentation — jiajunwu_cs · 2026-10-08
- Researchers compile a larval fish brain into a simulated circuit running 26x faster than spiking models — mtizard · 2026-10-08
- US Treasury issues first outbound investment fine: $200k over $92k Chinese robotics AI deal — pstAsiatech · 2026-10-08