CL-25 — mjlab continual learning
Benchmark previews
Random-agent clips of every registered task — scene layout and asset placement, not task solvability.
Phase 1 — classical teachers
Scripted-policy rollouts for the 25 Class-A tasks, each card showing the measured success rate against the 0.90 bar.
CL-V2 — realistic assets
Every Class-A task rebuilt with a real, textured, real-scale object or mechanism (YCB, Google Scanned Objects, Poly Haven, or built): asset still + turntable, verified physics / init distribution / success predicate, and the classical teacher re-measured at n = 128 with the new asset.
CL-V3 — teachers to ~100 %
The classical teachers re-strategised on the realistic assets, measured at n = 128 on a frozen initial-pose distribution (object yaw, mechanism yaw + height, robot joint noise); only tasks at or above 0.90 are published.