forge-gearmesh-v1

Forge GearMesh expert (PPO)

PPO policy for Isaac-Forge-GearMesh-Direct-v0 (Isaac Lab FORGE suite), trained with rl_games. Final return 740.3; measured success 12/12 in scripted video evaluation; ~99% over ~1,004 collection episodes.

What is in the file

rl_games checkpoints hold more than the policy: the actor-critic weights, the optimizer state, and — importantly for inference — the running observation normalisation statistics. That is why a small MLP policy takes 204 MB. Load it through the Isaac Lab player rather than torch.load alone, so the normaliser is restored with the weights:

./isaaclab.sh -p scripts/reinforcement_learning/rl_games/play.py \
    --task Isaac-Forge-GearMesh-Direct-v0 --checkpoint /path/to/forge_gearmesh_2026-08-01.pth --num_envs 1 --headless

Training

Stock Isaac Lab rl_games PPO, unchanged hyper-parameters except the epoch count: 128 environments, seed 0, 300 epochs (the shipped config stops at 200, which cuts the run while the return is still climbing). Snapshots exist at epochs 100 and 200; this repo ships epoch 300.

Related

• Demonstrations produced with this policy: angledusgar/forge-v1
• Its failures: angledusgar/forge-failure-v1