ctr-pi05-pick-dual-bottles-ctr50-full-idlemask-e301-step10000

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DEPRECATED / 已废弃 — E301

Do not reuse this checkpoint or treat evaluations derived from it as valid scientific results.

废弃原因：当前 CTR IdleMask 的定义/实现存在问题，该固定 checkpoint 使用了问题 mask 训练。

CTR datasets are the intended scope for IdleMask. This deprecation concerns the incorrect mask definition/implementation used for this checkpoint; it does not recommend disabling IdleMask for CTR. Historical training completion, checksum verification, success rates and archive approvals are preserved for diagnosis only and do not restore eligibility.

Correcting labels or changing inference settings cannot repair these trained weights. An explicitly audited corrected dataset revision, newly trained checkpoint and separate evaluation are required. No weights have been removed or modified by this metadata update.

Owner-authorized deprecation dated 2026-09-19. This notice supersedes earlier reuse or validity statements below.

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E301 — Pick Dual Bottles / ctr50_full

Inference checkpoint at 10000 optimizer updates. Training Run: E301-R001.

Closed-loop evaluation has not been reported for this checkpoint.

Training identity

• Dataset: Shiki42/ctr-pick-dual-bottles-ctr50-20260916
• Revision: f57bd3c2d34ca914c283def7d1272b4aa44b0634
• Camera preset: centered_fovy90 calibrated wrist cameras.
• IdleMask: enabled; inactive arm loss weight zero.
• Exact configuration and source identities: ctr-provenance.json and resolved configuration.

Loading and limitations

OpenPI JAX full parameter tree at pinned source e9ba7b7732a3e66e4bd87d6d3429f7cef6352ead. Use create_trained_policy with the included params, assets, matching resolved configuration and dataset asset ID. Joint targets are delta-encoded during training and decoded once; grippers are absolute commands. CTR loader and inference adapter are required for these RoboTwin conventions.

No optimizer state is included. SHA256SUMS identifies every public payload file. Source checkpoint and training logs remain preserved on the training machine. Training completion and raw evaluation result archival do not establish deployment safety or scientific causal conclusions.