| # closed-loop-checkpoints — policies + tactile tokenizer (T-Rex reproduction sim study) | |
| Checkpoints from the closed-loop Isaac Sim study in | |
| https://github.com/physercoe/trex-repro (`docs/CLOSED_LOOP_EVAL.md`). | |
| Task: press-in peg-in-hole, RH56 hand, Isaac Sim 5.1 / IsaacLab 2.3.2, | |
| tactile = ground-truth F6 wrenches. PyTorch `.pt` state dicts; architectures | |
| in `closed_loop/bc_policy.py` / `train_ppo.py` / `train_vqvae.py`. | |
| | file | role | eval SR (σ=20 mm clean / +disturbance) | | |
| |---|---|---| | |
| | `ppo3_champion_model_iter800_eval0973.pt` | blind PPO base ("champion", variant (b)); SR 0.973 on the clean task | 0.586 / 0.629 (frozen) | | |
| | `ppo4_blind_model_final.pt` | blind fine-tune control (b′) | 0.785 / 0.766 | | |
| | `ppo4_raw_model_0.pt` | raw-F6 tactile variant (c), ~iter 340 | 0.789 / 0.734 | | |
| | `ppo4_tac2_model_final.pt` | VQ-token tactile variant (a) | 0.754 / 0.746 | | |
| | `vqvae4_sim_tactile_vqvae.pt` + `vqvae4_norm_stats.npz` | tactile VQ-VAE tokenizer (30 Hz 16-step F6 windows → one-hot codes) + normalization stats | — | | |
| All fine-tunes used identical PPO budget (400 iters, 512 envs) from the | |
| champion. Verdict of the study: tactile VQ-token features gave no robust | |
| advantage over the same-budget blind control in this setup — see the repo for | |
| the full table and caveats. License: MIT. | |