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name: training description: "Route VPG model, training, testing, and resume work for reactive or reinforcement policies operating on RGB-D heightmaps; validate commands and artifacts before any long-running or hardware-bound loop." disable-model-invocation: true metadata: disco-role: operating license: BSD 2-Clause

VPG training

Use this route when the task is to inspect, train, test, or resume a VPG push/grasp policy from an RGB-D heightmap. The distilled evidence is pinned to source commit 580e2334beec0d83b49e6ca89d7542b79d1d4350, a historical Python 2/early-Python-3 checkout with no package metadata. It is an operating guide, not a claim that the complete loop is compatible with current Python, PyTorch, robot, or simulator stacks.

Scope and routing

• Own here: main.py flags and defaults, models.py/trainer.py model behavior, reactive versus reinforcement targets, action choice, rotations, preprocessing, exploration, replay, heuristics, snapshots, and transition logs.
• Route RGB-D projection, camera poses, workspace geometry, and heightmap construction to perception-geometry.
• Route simulator startup, scenes, meshes, and remote API failures to simulation.
• Route metric aggregation and plots to evaluation.
• Route physical camera, UR5 motion, TCP services, and calibration to real-robot.

Do not start the source application's main.py merely to inspect this skill. It opens a camera/robot adapter, may create a simulator or physical action loop, initializes networks, and runs indefinitely until an environment-specific stop condition. The bundled validator is deliberately side-effect free: check_training_config.py.

Install and import boundary

There is no package metadata or supported pip install target in the source artifact. In a separately prepared application environment, install the public README-level dependencies with an explicit environment policy rather than relying on this skill to modify it:

python -m pip install numpy scipy opencv-python matplotlib torch torchvision

A bounded current-Python numerical-stack check can report imports and an optional small CUDA allocation through <skill-root>/scripts/check_environment.py; those are compatibility observations, not full-loop or snapshot-compatibility proof. Historical source imports and source main.py/evaluate.py/plot.py help probes were construction evidence only and are not runtime instructions. torchvision model construction may attempt a pretrained-weight download; do not allow that network side effect during a safe check.

If this graph is imported into an agent skill directory, import the containing repo skill graph as one transaction through its root workflow; do not copy or import this sub-skill alone, and do not import it into a live router without separate approval. The runtime files here contain no source-checkout or machine-specific inspection dependency.

Operating procedure

1. Choose the method and environment. Use --method reactive for the classification/label variant, or --method reinforcement for the Q-value variant (the source default). Use --cpu for a correctness-oriented, slower path. CUDA is optional but practically important; verify it before allocating a long run.
2. Preflight without starting the loop. Run the validator with the same method, testing flags, snapshot, and session paths. It checks flag combinations, file types, continuation logs, and numeric ranges without importing torch, loading a state dict, downloading weights, opening a socket, or creating directories.
3. Prepare RGB-D inputs. The upstream geometry route must supply an RGB heightmap (H,W,3) and depth heightmap (H,W) in meters. Empty depth cells are converted to zero before the trainer; retain geometry/calibration provenance separately.
4. Select and execute actions. A volatile forward pass produces push and grasp maps for 16 rotations. The main loop selects the maximum map entry, maps (rotation, y, x) to an action, and sends it to the environment. Keep the simulator/robot prerequisite and safety confirmation outside this route.
5. Train or test deliberately. Testing disables exploration and replay in the loop and stops only after the simulator/test clearance count reaches --max_test_trials; it still needs a real environment. Training writes a backup snapshot every iteration and a numbered snapshot every 50 steps. Testing does not save snapshots.
6. Resume only from a complete pair. Use a compatible model state dict plus --continue_logging pointing at the exact prior session. Validate all transition logs before allowing the loop to read them. Stop if the snapshot cannot load, a log is missing/truncated, or the method differs from the snapshot architecture.

Safe test template

First validate a bounded, one-trial configuration; this command does not run training or testing:

Let <skill-root> mean the directory containing the root SKILL.md. This side-effect-free preflight uses only the bundled helper; <MESH_DIR>, <CASE>, <SNAPSHOT>, and <LOG_DIR> are operator-supplied external paths:

python <skill-root>/sub-skills/training/scripts/check_training_config.py \
  --method reinforcement --is_testing --max_test_trials 1 --cpu \
  --is_sim --obj_mesh_dir <MESH_DIR> --test_preset_cases \
  --test_preset_file <CASE> --load_snapshot --snapshot_file <SNAPSHOT> \
  --logging_directory <LOG_DIR>

Only after an operator has verified the external simulator/scene, object assets, snapshot provenance, and stop plan should a separately prepared application launch be attempted. Use python <APP_ROOT>/main.py with the same flags, where <APP_ROOT> is an operator-supplied, separately reviewed application root—not this runtime graph. A live command is intentionally not presented as a successful recipe: the graph supplies no simulator, robot, pretrained weights, or historical application loop. Use one trial and stop on the first unexpected action, missing frame, or load warning.

References and helper

• CLI flags and safe recipes
• Model, preprocessing, rewards, and updates
• Logging, snapshots, and resume
• Training troubleshooting and stop boundaries
• Safe flag/snapshot/log validator

Source files named in these references are source artifacts used for evidence; they are not bundled runtime modules and should not be invoked from the original checkout as a verification shortcut.