[Submitted on 4 Sep 2026]
Title:Physical Kernel: Structured Visual Latents for Dark Manipulation
View a PDF of the paper titled Physical Kernel: Structured Visual Latents for Dark Manipulation, by Jinting Hang and 4 other authors
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Abstract:We study dark manipulation: after a brief lit Write encodes z0 = Enc(rgb), a policy pi(z) and open-loop dynamics f(z,a) complete contact-rich skills without further pixels (dark_f). On ManiSkill StackCube (n=160; seed packs 0/1000), dark_f attains 68.1% stacked on the five-rung chain (near_A -> grasped -> lifted -> on_B -> stacked), compared with 35.6% for per-step lit_reenc and 0% for freeze/encode_black. On a shared Write->HOLD protocol (n=40), occlusion and camera-aligned GT contact-neighbor masks drive lit lift from 43% to 0%, while dark_f holds 82.5%; shuffling actions inflates dynamics MSE by ~9.4x; write-time appearance shifts break encoding (night: 0% stacked), yet the same shifts during HOLD leave dark_f lift unchanged; Write length Tw is flat once the stop phase is reached, while earlier stops and write-time blur/JPEG sharply cut stacked. A dedicated pi_write reaches 35% vision-budget stacked (n=80); matched Dreamer-style/pixel nulls without privileged geom stay at 0%. Privileged state-RSSM MPC reaches ~35% stacked with 9D dark observations -- a stronger-observation null, not a matched visual baseline.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.13244 [cs.RO]
(or arXiv:2609.13244v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.13244
arXiv-issued DOI via DataCite
Submission history
From: Jinting Hang [view email] [v1] Fri, 4 Sep 2026 08:28:21 UTC (547 KB)
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