Rename policy directory to Bagel_Robotics_1

README.md

@@ -39,11 +39,11 @@ There are approximately 29.0 GB of checkpoint tensors. Keep all six files togeth
 
 ## Inference adapter and reevaluation
 
-The standalone XPolicyLab adapter is provided under [`inference/`](inference/), at release tag `bagel-robotics-1-cp1000-inference-v1`. No private research repository is required.
+The standalone XPolicyLab adapter is provided under [`inference/`](inference/), at release tag `bagel-robotics-1-cp1000-inference-v2`. No private research repository is required.
 
 ```bash
 hf download bageldotcom/Bagel-Robotics-1 \
-  --revision bagel-robotics-1-cp1000-inference-v1 \
+  --revision bagel-robotics-1-cp1000-inference-v2 \
   --include 'inference/**' \
   --local-dir /absolute/path/to/Bagel-Robotics-1-release
 
@@ -52,9 +52,9 @@ git -C /absolute/path/to/workspace/XPolicyLab checkout 6baccd57ff958570d0db15883
 python /absolute/path/to/Bagel-Robotics-1-release/inference/install_adapter.py /absolute/path/to/workspace/XPolicyLab
 ```
 
-Follow the included [policy README](inference/policy/bagel_robotics_1/README.md) to install the Python 3.10 environment, download/verify assets and run both debug checks. Follow [REEVALUATION.md](inference/policy/bagel_robotics_1/REEVALUATION.md) for task, suite and three-seed commands. Install RoboDojo and simulator assets separately using its [official guide](https://robodojo-benchmark.com/doc/usage/install-and-download/).
+Follow the included [policy README](inference/policy/Bagel_Robotics_1/README.md) to install the Python 3.10 environment, download/verify assets and run both debug checks. Follow [REEVALUATION.md](inference/policy/Bagel_Robotics_1/REEVALUATION.md) for task, suite and three-seed commands. Install RoboDojo and simulator assets separately using its [official guide](https://robodojo-benchmark.com/doc/usage/install-and-download/).
 
-The adapter uses `policy_name=bagel_robotics_1`, `ckpt_name=cp1000`, ARX X5 end-effector actions, and one environment per policy process. Fixed inference settings: BF16; predict 32 actions and execute 16; ten Euler steps; shift 1; 90% new / 10% previous temporal blending. Qualification used a 96 GB RTX PRO 6000 Blackwell, Python 3.10.21 and PyTorch 2.11.0+cu130; a lower minimum GPU capacity has not been qualified.
+The adapter uses `policy_name=Bagel_Robotics_1`, `ckpt_name=cp1000`, ARX X5 end-effector actions, and one environment per policy process. Fixed inference settings: BF16; predict 32 actions and execute 16; ten Euler steps; shift 1; 90% new / 10% previous temporal blending. Qualification used a 96 GB RTX PRO 6000 Blackwell, Python 3.10.21 and PyTorch 2.11.0+cu130; a lower minimum GPU capacity has not been qualified.
 
 Use the Bagel-Robotics-1 compact checkpoint loader supplied in the inference package. The implementation and current reviewer instructions are available in [XPolicyLab PR #234](https://github.com/XPolicyLab/XPolicyLab/pull/234).
 

THIRD_PARTY_NOTICES.md

@@ -1,12 +1,12 @@
 # Source and artifact notices
 
 - Reused source components from [VPP2](https://github.com/roboterax/video-prediction-policy-2)/FastWAM retain the MIT notice in
-  [LICENSE](inference/policy/bagel_robotics_1/LICENSE), copyright 2026 The FastWAM Authors. See [source-provenance.json](inference/policy/bagel_robotics_1/source-provenance.json)
+  [LICENSE](inference/policy/Bagel_Robotics_1/LICENSE), copyright 2026 The FastWAM Authors. See [source-provenance.json](inference/policy/Bagel_Robotics_1/source-provenance.json)
   for the source identities and packaging changes.
 - Wan2.1/DiffSynth-derived model components retain Apache-2.0 terms; see
-  [licenses/Apache-2.0.txt](inference/policy/bagel_robotics_1/licenses/Apache-2.0.txt) and upstream Wan2.1/DiffSynth source notices.
+  [licenses/Apache-2.0.txt](inference/policy/Bagel_Robotics_1/licenses/Apache-2.0.txt) and upstream Wan2.1/DiffSynth source notices.
 - The XPolicyLab interface follows its upstream license, included in
-  [licenses/XPolicyLab.txt](inference/policy/bagel_robotics_1/licenses/XPolicyLab.txt). RoboDojo and simulator assets are not bundled.
+  [licenses/XPolicyLab.txt](inference/policy/Bagel_Robotics_1/licenses/XPolicyLab.txt). RoboDojo and simulator assets are not bundled.
 - Encoder weights, tokenizer files and model weights have their own upstream
   terms. The code license does not assign a new license to those artifacts.
 - The existing preview PR by Soroush Mehraban was consulted as a reference; its

inference/README.md

@@ -2,7 +2,7 @@
 
 Bagel-Robotics-1 combines mask-based structured pruning, recovery training and task-critical data selection to produce a compact WAM with nonuniform FFN widths and cross-attention head counts. Its inference pipeline predicts 32 actions, executes 16 before replanning, and applies 90/10 temporal ensembling. VPP2 is the validation base; reused source components are credited in the policy's source notices.
 
-This folder is the standalone XPolicyLab adapter for Bagel-Robotics-1 checkpoint 1000. It contains inference code and evaluation instructions, with no training repository dependency. Model tensors download separately from `bageldotcom/Bagel-Robotics-1`, pinned to `2651631c5d08819088475e8778676f5cf6d9afcf` and verified against `policy/bagel_robotics_1/artifact-manifest.json`.
+This folder is the standalone XPolicyLab adapter for Bagel-Robotics-1 checkpoint 1000. It contains inference code and evaluation instructions, with no training repository dependency. Model tensors download separately from `bageldotcom/Bagel-Robotics-1`, pinned to `2651631c5d08819088475e8778676f5cf6d9afcf` and verified against `policy/Bagel_Robotics_1/artifact-manifest.json`.
 
 Install this adapter into an existing XPolicyLab checkout (the installer refuses to overwrite an existing policy):
 
@@ -10,7 +10,7 @@ Install this adapter into an existing XPolicyLab checkout (the installer refuses
 python install_adapter.py /absolute/path/to/XPolicyLab
 ```
 
-Then follow [Installation](policy/bagel_robotics_1/README.md#installation) for the Python 3.10 environment, exact checkpoint and encoder download, and debug checks. [REEVALUATION.md](policy/bagel_robotics_1/REEVALUATION.md) gives native simulator task/suite/three-seed commands. RoboDojo and its assets are installed separately.
+Then follow [Installation](policy/Bagel_Robotics_1/README.md#installation) for the Python 3.10 environment, exact checkpoint and encoder download, and debug checks. [REEVALUATION.md](policy/Bagel_Robotics_1/REEVALUATION.md) gives native simulator task/suite/three-seed commands. RoboDojo and its assets are installed separately.
 
 The adapter is prepared against XPolicyLab `6baccd57ff958570d0db1588370289ee36c8bb70`; source and dependency pins are preserved in the policy folder. The adapter is under review in [XPolicyLab PR #234](https://github.com/XPolicyLab/XPolicyLab/pull/234). Training/pruning/export source and the technical report are planned for November 20, 2026.
 

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inference/install_adapter.py

@@ -19,8 +19,8 @@ def main():
     for rel in ["model_template.py", "setup_policy_server.py", "utils/checkpoint_resolver.py"]:
         if not (xpl / rel).is_file():
             parser.error(f"Not a compatible XPolicyLab checkout: missing {rel}")
-    source = ROOT / "policy/bagel_robotics_1"
-    destination = xpl / "policy/bagel_robotics_1"
+    source = ROOT / "policy/Bagel_Robotics_1"
+    destination = xpl / "policy/Bagel_Robotics_1"
     if destination.exists():
         parser.error(f"Refusing to overwrite {destination}; choose a clean checkout")
     shutil.copytree(source, destination,

inference/policy/{bagel_robotics_1 → Bagel_Robotics_1}/EVALUATION_RESULTS.md

@@ -1,4 +1,4 @@
-# Checkpoint-1000 research evaluation results
+# Bagel-Robotics-1 results
 
 Bagel-Robotics-1 / AdaPrune Final, checkpoint 1000. Success means full task completion; partial scores average credited progress over all reported outcomes, including failures. Partial values here are out of 100. Generalization combines standard and randomized episodes; other suites use their recorded standard scene selections.
 

inference/policy/{bagel_robotics_1 → Bagel_Robotics_1}/README.md

@@ -18,7 +18,7 @@ git -C "$BAGEL_XPL_ROOT" fetch https://github.com/XPolicyLab/XPolicyLab.git refs
 git -C "$BAGEL_XPL_ROOT" checkout --detach FETCH_HEAD
 git -C "$BAGEL_XPL_ROOT" rev-parse HEAD
 
-BAGEL_POLICY_DIR="$BAGEL_XPL_ROOT/policy/bagel_robotics_1"
+BAGEL_POLICY_DIR="$BAGEL_XPL_ROOT/policy/Bagel_Robotics_1"
 BAGEL_POLICY_ENV="$BAGEL_POLICY_DIR/.venv"
 cd "$BAGEL_POLICY_DIR"
 PYTHON_BIN=python3.10 bash install.sh "$BAGEL_POLICY_ENV"

inference/policy/{bagel_robotics_1 → Bagel_Robotics_1}/REEVALUATION.md

@@ -41,7 +41,7 @@ example selects GPU 0. Do not overwrite a scheduler's GPU visibility assignment.
 
 ```bash
 BAGEL_XPL_ROOT=/absolute/path/to/XPolicyLab
-BAGEL_POLICY_DIR="$BAGEL_XPL_ROOT/policy/bagel_robotics_1"
+BAGEL_POLICY_DIR="$BAGEL_XPL_ROOT/policy/Bagel_Robotics_1"
 BAGEL_POLICY_ENV="$BAGEL_POLICY_DIR/.venv"
 BAGEL_SIM_ROOT=/absolute/path/to/RoboDojo
 BAGEL_SIM_ENV=your_simulator_conda_env

inference/policy/{bagel_robotics_1 → Bagel_Robotics_1}/deploy.yml

@@ -1,4 +1,4 @@
-policy_name: bagel_robotics_1
+policy_name: Bagel_Robotics_1
 protocol: ws
 host: localhost
 port: null

inference/policy/{bagel_robotics_1 → Bagel_Robotics_1}/evidence/pr-audit.json

@@ -11,10 +11,13 @@
   },
   "pr_template_headings_exact": true,
   "gpu_qualification": "passed",
-  "ready_for_pr": false,
+  "ready_for_pr": true,
+  "pr_creation_blockers": [],
+  "readiness_scope": "Opening a review PR; not maintainer acceptance or verified leaderboard publication",
   "remaining_gates": [
-    "dedicated private reviewer access",
     "maintainer agreement on proposed eval-only integration and release timeline",
-    "user review before PR creation"
+    "assigned evaluator and private credential handoff",
+    "official multi-seed evaluation and leaderboard review",
+    "public artifact release for verified leaderboard publication"
   ]
 }