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{
"streams": [
[
"view",
"camera view"
],
[
"tl",
"tactile left"
],
[
"tr",
"tactile right"
]
],
"metrics": [
[
"psnr",
"PSNR (dB)",
1
],
[
"ssim",
"SSIM",
1
],
[
"lpips",
"LPIPS",
-1
]
],
"arms": [
{
"key": "delta",
"label": "delta_action + isolate",
"job": "514906",
"ckpt": "best-epoch=38-val_loss=0.0901.ckpt",
"note": "per-step rows (force f[k+1]-f[k]); gels do not attend each other",
"wandb": {
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"val_loss_video": 0.06267819553613663,
"val_loss_tactile": 0.032351139932870865,
"train_loss_epoch": 0.06542341411113739
},
"best_val": 0.0901
},
{
"key": "chunk",
"label": "action_chunking + isolate",
"job": "514907",
"ckpt": "best-epoch=33-val_loss=0.0902.ckpt",
"note": "anchor-relative rows (force f[k+1]-f[0]); gels do not attend each other",
"wandb": {
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},
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},
{
"key": "noisolate",
"label": "delta_action, no isolate",
"job": "516058",
"ckpt": "best-epoch=33-val_loss=0.0901.ckpt",
"note": "same as 514906 with isolate_sensors: false -- the gels attend each other",
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},
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}
],
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{
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},
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}
},
{
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}
},
{
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"tr_psnr": true,
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}
}
],
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{
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}
}
],
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{
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{
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]
},
{
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"all": 16.011046508034084,
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"hl": false
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{
"a": "dforce_delta_fps15",
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"p": 7.555811484431396e-05,
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"p": 0.14599609375,
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"p": 0.3876953125,
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},
"title": "delta_action vs action_chunking (the action representation)",
"line": "data.action_repr",
"a_label": "delta_action + isolate",
"b_label": "action_chunking + isolate"
},
{
"a": "dforce_delta_fps15",
"b": "dforce_noisolate_fps15",
"all": {
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"p": 0.28878428041410487,
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"p": 0.8679394004284404,
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"1": {
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},
"title": "isolate_sensors true vs false (do the two gels attend each other?)",
"line": "model.isolate_sensors",
"a_label": "delta_action + isolate",
"b_label": "delta_action, no isolate"
}
]
},
"lede": "Three runs encode the tactile force as a <b>change</b> of indentation (<code>data.force_term: delta</code>, k_f&middot;(f[t+1]&minus;f[t])) on the 15 fps latent tree, with no pixel-flow branch. They form two one-line comparisons that share a corner: <code>data.action_repr</code> (per-step <code>delta_action</code> vs anchor-relative <code>action_chunking</code>) and <code>model.isolate_sensors</code> (may the two gels attend each other?). Same data, same seed, and the held-out rollouts below draw the same 24 windows in every arm (asserted, not assumed).",
"verdict": "<b>The action representation splits by stream, and the cross-sensor attention mask costs tactile accuracy.</b> <code>delta_action</code> beats <code>action_chunking</code> on the camera view (24.30 vs 23.99 dB, 18&ndash;20 of 24 windows) and on commanded actions (5.5 vs 6.9 px median translation error), while <code>action_chunking</code> wins the tactile images (right gel 42.23 vs 41.47 dB, 5 of 24). Turning the isolation mask OFF &mdash; letting the two gels attend each other again &mdash; improves both gels (right gel 42.30 vs 41.47 dB; SSIM wins 20/24 and 19/24 for the unmasked arm, p&le;0.007) at no measurable cost to the view (15/24, p=0.31). <b>This is the first time the isolate ablation has moved anything</b> &mdash; it tied on val loss at 6 fps and 15 fps with the absolute force term, and it ties again here (0.0901 / 0.0902 / 0.0901). On the probes its median favours the masked arm (5.2 vs 7.0 px) but the paired count is only 41 of 72 (p=0.29) and flips sign on two of the six start frames, so that gap is not supported.",
"diff": "data:\n force_term: delta # both arms\n action_repr: delta_action # job 514906\n action_repr: action_chunking # job 514907\n\ndelta_action row k = pose step idx[k] -> idx[k+1], force f[k+1] - f[k]\naction_chunking row k = pose idx[0] -> idx[k+1], force f[k+1] - f[0]\n (idx[0] = the window's first frame, the anchor)",
"diff_note": "The two are the same converter on a different pose pair; a window's delta rows telescope to the chunk's last row. They also compress differently into the 3 latent transitions the model conditions on: <code>delta_action</code> average-pools the 5 frame rows of a block, <code>action_chunking</code> takes the block's LAST row (averaging anchor-relative rows would be meaningless).",
"loss_note": "Both ran 100 epochs on 2xH100 and overfit after ~ep35, so these are the best-* checkpoints, not <code>last.ckpt</code>: final-epoch val is 0.0950 / 0.0949. The run's split file warns that val aliases test, so treat every number here as a development metric.",
"metric_note": "<b>Predicted frames only.</b> The rollout is given 2 context latent frames (decoded frames 0&ndash;4) and generates 2 more (frames 5&ndash;12); averaging over all 13 frames, as the older decode script does, adds ~2 dB of free PSNR from frames that are identical by construction. Green = better of the two arms.",
"sign_note": "Paired over the same 24 held-out windows. Windows inside one episode overlap in time, so they are not fully independent and the p-values are optimistic; the win counts are the more honest summary. 24 windows can at best reach p=1.2e-7.",
"probe_note": "React <code>probes_v1</code>: 72 commanded axis-aligned motions from 6 real start frames, scored geometrically by tracking the gel centre through the decoded video with AllTracker (median final error, crop px; lower is better). All models on one table, but the 15 fps rows measure a 0.53 s horizon and the 6 fps rows 1.33 s &mdash; compare within a rate only. Do-nothing scores 38.7 px on translations.",
"pair_note": "The 72 probes are 6 start frames x 12 directions, and probes sharing a start frame share context and episode, so the 'all 72' p-value treats correlated samples as independent. Read the per-start-frame rows: delta is ahead on 5 of 6.",
"strip_note": "Row 1 is the model's prediction, row 2 the ground truth, one column per decoded frame. Frames 0&ndash;4 come from the 2 given context latents (they are GT in both rows); the red box marks frame 5, the first generated one.",
"caveats": [
"<b>The isolate comparison is a mask-only change</b>: no parameter count or weight shape changes, so the two arms differ solely in which attention edges exist. The same ablation has come out a tie on val loss twice before (fps6 and fps15, absolute force); this is the third setting it is asked in.",
"<b>The delta arms' force channel is dead on these probes.</b> With the commanded future force held constant, every per-step row has &Delta;f = 0, so <code>act_wdforce_hold</code> IS the plain rigid action. The chunk arm still sees f_commanded &minus; f_anchor. The two arms are not on equal footing on the force channel &mdash; that is a property of the encodings, not of the eval.",
"<b>The absolute-force reference rows are not paired</b> with the dforce cells: they were rolled out on 10 windows drawn independently, and random.sample(N, 24) does not extend random.sample(N, 10).",
"<b>Only the two named pairs are controlled.</b> action_chunking+isolate against delta_action-without-isolate differs in two lines at once; the summary table shows all three arms side by side, but read a difference only along one axis.",
"<b>Probe grid offset.</b> For start frames run1 and run4 the probe's commanded pose p[0] sits one native frame off the window grid, up to 10.3 mm from the real pose at t_ctx. The spliced per-step stream silently drops that jump; the anchor-relative rows carry it. Pre-existing, and it hits the absolute-force runs identically.",
"<b>Bug fixed to make this eval valid:</b> <code>scripts/inference_rollout_mot.py</code> called <code>_compress_action(act, W_lat)</code> without <code>action_repr</code>, so every <code>action_chunking</code> run was rolled out with average-pooled anchor-relative rows instead of block-last ones. Any earlier short-horizon eval of a chunk run is suspect.",
"Single seed (0) for both the rollouts and the probes."
],
"repro": "# held-out rollout + PSNR/SSIM/LPIPS + PRED-over-GT strips; ARMS_SPEC selects the\n# arms and the cells are idempotent, so an added arm reuses the existing ones\nsbatch vm_diffusion/run_eval_dforce_ab.sh\n\n# commanded rows for the delta encoding (act_wdforce_* / act_chunk_wdforce_*)\nPROBE_FPS=15 python -m vm_diffusion.probe_eval.add_dforce_actions\n\n# React probe eval (MODELS_SPEC picks the arms; default = the first two)\nsbatch vm_diffusion/probe_eval/run_probe_eval_dforce.sh\n\n# this page\npython -m vm_diffusion.scripts.build_hf_space_dforce --push\n"
}