ACRFT YAM patch-critics (s347)
Frozen-DINOv2 patch critics trained on the YAM lego-taxi teleop set (347 episodes: 300 success / 47 failure), for critic-guided rollouts (best-of-N / adaptive-chunk selection) on top of a pi0.5 BC policy. All arms share the house value conventions: cost_to_goal reward (-1/step), gamma 0.99964, support [-2777.8, 0] (HL-Gauss 101 atoms), h_goal=30 absorbing goal, homing tails truncated from every episode, K=2 ensemble, 200k steps, batch 256, lr 1e-4.
Input contract (per config.json input_spec): 3 cameras (agentview + both wrists) at 224,
pi0.5-normalized (quantile) state, pi0.5-normalized JOINT-DELTA action chunks (horizon 30),
proprio = joint positions + grippers (14) unless noted. The serving wrapper reproduces this
preprocessing from the spec automatically.
Arms
| folder | chunking | tau | q_reduction | feat aug | note |
|---|---|---|---|---|---|
| patch_critic_yam_s347_fixed_200k | fixed (single prefix H=30) | 0.7 | min | none | DEAS-style fixed-chunk IQL baseline |
| patch_critic_yam_s347_g5_200k | adaptive (prefixes 5..30) | 0.7 | min | none | adaptive-chunk IQL (ARQ) |
| patch_critic_yam_s347_g5_tau9_min | adaptive | 0.9 | min | 0.1/0.05 | hindsight-leakage-reduced arm |
| patch_critic_yam_s347_g5_tau9_mean | adaptive | 0.9 | mean | 0.1/0.05 | leakage-reduced, mean ensemble |
| patch_critic_yam_s347_g5all_200k | adaptive | 0.7 | min | none | proprio ALL(42) incl. velocity/effort - extra sensors, NOT baseline-fair |
Dataset-side evaluation (stride-20, 347 eps, scorer 673f33a)
| fixed_200k | g5_200k | |
|---|---|---|
| success V(s0) | -1620.7 | -1620.4 |
| failure V(s0) | -2580.4 | -2606.8 |
| success Vslope (vs true cost-to-go) | 0.85 | 0.90 |
| success V at goal-1 | -298.5 | -36.6 |
| k* success / failure | 30 / 30 (trivial) | 12.8 / 6.1 |
Known caveat: the tau=0.7 no-aug arms carry strong hindsight leakage (success/failure V(s0) gap ~1000 where the behaviour-policy ideal is 0); the tau9 arms reduce that gap by ~65%. Compare arms relatively; do not read absolute values as calibrated returns.
Rollout / serving
uv run python scripts/serve_patch_critic.py \
--config pi05_yam_lego_taxi --ckpt <pi05 BC checkpoint> \
--critic-dir <downloaded critic folder> \
--num-samples 8 --mode adaptive # or --mode bon
(alpha-Flow policy: add --config pi05_yam_lego_taxi_alphaflow --flow-steps 1 for one-step
sampling; see jellyho/pi05_yam_lego_taxi_alphaflow.)
Trained by the ACRFT patch-critic session (code: jellyho/ACRFT integration branch, scripts/train_patch_critic_cached.py; feature cache yam_s347). An mc_floor-off pair (fixed/g5) is training and will be added when complete.