Vial sort← Latest work

Previous work, August 2026

The first complete system: 150 teleoperated demonstrations on the Jetson Thor rig with three cameras, and a π0.5 policy that follows source-to-destination commands.

The policy keeps π0.5's vision–language backbone frozen and trains only its action expert. A person scored 79 of its rollouts over 15 commands; the numbers below come from that evaluation. The newer π0.5 and FLUX runs, on a workstation with five cameras, are the latest work.

How well it worked

  • 25%completed the whole pick-and-place20 of 79 rollouts
  • 8%acted on the wrong vial or slot6 of 79 rollouts
  • 54%missed the first grasp of the vial43 of 79 rollouts
  • 44%completed the move once the first grasp held16 of 36 rollouts

Everything from August

The policies tried in August

Only π0.5 was deployed and evaluated; the others were set up on the same rig and not carried through.

PolicyWhat was trainedDataStepsTraining timePeak GPU memoryOn the robotSource
The August policies (Thor rig, three cameras)
π0.5, frozen backbone
π0.5
Action expert only; vision-language backbone frozenvial-sort-v5, 150 demos, 3 cameras30k (12k deployed)not loggednot logged20 of 79 rollouts complete, scored by a person.report
π0
π0
Action expert, full fine-tune vs LoRA; backbone frozenvial-sort-v1, 100 demos30kabout 5–6 h eachsame for bothSame training loss, but LoRA sometimes missed the grasp; full fine-tune grasped reliably. Only one fixed command was tried.project notes (June 2026), report
GR00T N1.5
GR00T N1.5
Fine-tune, batch 16vial-sort-v420k3 h 58 min54.7 GBExploratory; not carried through to deployment.project notes (July 2026), report
ACT
ACT
–––not loggednot loggedNo language input; a motion baseline only.report