slop

plans/rover learning service.md

@@ -2,27 +2,21 @@
 
 ## Resume here
 
-Latest change: format 3 command-event policy replaces format 2 continuous wheel
-speeds. All supported controls, including drive and recorded zero-speed stops,
-use scored command templates and the same threshold/checked command pipe. The
-network produces scores and numeric values; it never generates JSON text.
+Latest change (2026-09-29 IPI): format 4 seeds known user-control command shapes
+before training. Generic peripherals are excluded; sensorStream is ignored without
+invalidating footage. Drive includes zero-speed drive, with no separate stop type.
+Drive, camera, brushes/vacuum, headlight, laser, horn, songs and operating modes
+remain supported. Raw recordings and the existing checked command pipe stay intact.
 
-Implemented: remove wheel-speed telemetry and previous drive-command history
-from inputs (raw recordings unchanged); preserve exact numeric zeros; reuse
-recordings with a fresh resume-v4.pt / schedule-v4.json lineage. Startup deletes recognized older-format snapshots/checkpoints and old training
-lineages; recordings and current-format models are preserved. One ongoing learner, gated
-Latest/Previous, and admin-created permanent snapshots remain.
+Held-out data cannot expand the schema. Unknown control variants exclude nearby
+windows only, with command/count/reason diagnostics and a coverage gate that blocks
+promotion. Supported portions still receive evaluation. Current training lineage
+is resume-v5.pt / schedule-v5.json. Startup removes obsolete format 1-3 models and
+old lineages through v4; recordings and current-format models are preserved.
 
-Main card shows decoded command parameters, score, applied threshold marker, and
-submission results, including below-threshold proposals. Diagnostics reportVersion
-3 includes command predictions/submissions and event evaluation. Manual inputs
-still never stop or pause autonomy. Disabled remains dormant/off by default.
-
-Focused Node syntax, Python production worker entry points, and card ESLint
-passed. New policy: 803,440 parameters, 30 sensor fields, stateSize 700. No live
-recordings/camera/rover pipeline or authenticated browser were available locally.
-Actual training, stop behavior, concurrent performance and visual appearance
-remain unverified. No tests were added and no processes were left running.
+Verification for this increment is recorded at the end. Earlier version-specific
+notes are historical. Manual inputs never pause/cancel autonomy; disabled service
+remains dormant. Actual autonomous performance has not been established.
 
 This document preserves the conversation's decisions across compactions and
 usage cutoffs. Keep the checklist and handoff section current during authorized
@@ -930,3 +924,41 @@ Cleanup runs even when training is disabled, but not when the whole service is o
 Production deletion occurs on the next enabled-service startup after deployment.
 Verification: focused Node syntax and diff whitespace checks passed. No production
 storage was accessed; actual deletion awaits enabled-service startup on the server.
+
+### User-control scope and evaluation coverage (2026-09-29)
+
+Authorized by latest IPI after user clarified that controls include camera, brushes,
+lights, laser, horn and songs but NOT generic peripherals. No new command pipe or
+separate stop command. sensorStream is neither input/target nor a boundary. Actual
+reset/reboot/update remain temporal boundaries because they interrupt observations.
+
+Format 4 seeds drive and motor zero/nonzero field combinations, servo, light/laser
+actions, the two four-frequency horn waveforms and horn release, OI start/safe/full/
+dock bytes and default song. Seed values describe shapes, not artificial training
+examples. Horn frequencies are numeric parameters; integral JSON numbers share
+shapes with equivalent floating representations for drive/motors/servo/horn. Songs
+remain exact demonstrated note arrays; other song/raw variants can extend the
+training schema within its existing 64-slot bound. No promise to pre-enumerate all
+possible tunes. Seeded slots receive negative examples even in sessions where that
+control was not used, avoiding untrained random seeded outputs.
+
+Evaluation freezes the training schema. Unsupported commands are reported with
+payload, reason, occurrence count (up to 32 distinct descriptions), total count and
+affected-window count. The affected observation history/target interval is excluded
+without labeling the unknown command as inaction or skipping the entire recording.
+Any unsupported held-out command blocks promotion; metrics describe only supported
+windows. If none survive, dataset diagnostics still report the coverage problem.
+Existing permission checks, threshold mechanics, recording and manual input behavior
+are unchanged. Generic peripherals are also removed from execution allowlist and
+rover compatibility profile. Previous policy models are automatically cleaned up;
+no configuration edit is required.
+
+Verification completed: train.py --check-dependencies loaded the production
+worker and constructed format 4 with 803,440 parameters and predefined command
+shapes. infer.py --help, Node syntax checks for the four changed service modules,
+focused VipRoverLearningCard ESLint and diff whitespace checks passed.
+No live server data/hardware/browser available locally. Actual training, driving,
+and browser appearance remain unverified. No new tests or background processes.
+Deploying starts a new learner using existing recordings; enabled-service startup
+removes older policy models and optimizer/scheduler lineages through v4. Current
+lineage is v5. No settings changes or prescribed human driving are required.

server/src/services/roverLearningService/capabilities.js

@@ -1,9 +1,9 @@
 // Match actuator configuration, not a rover's display name or changing telemetry.
 const { isDeepStrictEqual } = require('node:util');
-const POLICY_VERSION = 3;
+const POLICY_VERSION = 4;
 
 function controlProfile(meta = {}) {
-  return Object.fromEntries(['cameraServo', 'peripherals', 'headlight', 'laser', 'roomba', 'platform']
+  return Object.fromEntries(['cameraServo', 'headlight', 'laser', 'roomba', 'platform']
     .map((key) => [key, meta?.[key] ?? null]));
 }
 

server/src/services/roverLearningService/driving.js

@@ -9,7 +9,7 @@ const { recordCommandRequest } = require('../commandService/recording');
 const { acquireModel } = require('./models');
 const { compatible } = require('./capabilities');
 
-const OPERATING = new Set(['drive', 'motors', 'servo', 'headlight', 'laser', 'horn', 'peripheral', 'song', 'raw']);
+const OPERATING = new Set(['drive', 'motors', 'servo', 'headlight', 'laser', 'horn', 'song', 'raw']);
 const clamp = (value, min, max) => Math.max(min, Math.min(max, Number(value) || 0));
 
 function canControl(socket, roverId) {

server/src/services/roverLearningService/models.js

@@ -142,8 +142,8 @@ function removeObsoleteModels(root) {
       else { await fs.rm(target, { recursive: true, force: true }); retired.delete(target); }
     }
     // These filenames belong to superseded optimizer/scheduler lineages, not
-    // policy versions. The current command policy uses lineage v4.
-    for (const suffix of ['', '-v1', '-v2', '-v3']) {
+    // policy versions. The current command policy uses lineage v5.
+    for (const suffix of ['', '-v1', '-v2', '-v3', '-v4']) {
       for (const filename of [`resume${suffix}.pt`, `resume${suffix}.tmp`,
         `schedule${suffix}.json`, `schedule${suffix}.json.tmp`]) {
         await fs.rm(path.join(root, 'training', filename), { force: true });

server/src/services/roverLearningService/training.js

@@ -33,7 +33,7 @@ function createTrainer({ root, config, actionThreshold, logger }) {
         }
       }
       try {
-        const saved = JSON.parse(await fs.readFile(path.join(trainingRoot, 'schedule-v4.json'), 'utf8'));
+        const saved = JSON.parse(await fs.readFile(path.join(trainingRoot, 'schedule-v5.json'), 'utf8'));
         ledger = saved.sessions;
         state = { ...saved.progress, status: 'waiting' };
         if (!ledger || typeof ledger !== 'object') throw new Error('Invalid training schedule');
@@ -46,7 +46,7 @@ function createTrainer({ root, config, actionThreshold, logger }) {
   }
 
   async function saveLedger() {
-    const filename = path.join(trainingRoot, 'schedule-v4.json');
+    const filename = path.join(trainingRoot, 'schedule-v5.json');
     await fs.writeFile(`${filename}.tmp`, JSON.stringify({ sessions: ledger, progress: state }));
     await fs.rename(`${filename}.tmp`, filename);
   }

server/src/services/roverLearningService/workers/dataset.py

@@ -47,7 +47,7 @@ def load_events(directory):
     return metadata, sorted(events, key=lambda e: e['ts'])
 
 
-def prepare(directory, schema, stats):
+def prepare(directory, schema, stats, extend):
     metadata, events = load_events(directory)
     requests = {}
     accepted = set()
@@ -72,14 +72,27 @@ def prepare(directory, schema, stats):
     if not sensors:
         raise ValueError('No sensor observations')
     candidate = copy.deepcopy(schema)
-    history, targets = [], []
+    history, targets, unsupported_times = [], [], []
+    unsupported = {}
     stats.update({'sensorFrames': len(sensors), 'dispatches': len(dispatches),
                   'failedCommands': len(failed), 'sensorPresentFraction': np.mean([row[1][len(SENSORS):] for row in sensors], axis=0).tolist()})
     for data in dispatches:
         command = data.get('command') or {}
-        if command.get('type') in ('reset', 'reboot', 'update', 'sensorStream'):
+        if command.get('type') in ('reset', 'reboot', 'update'):
             boundaries.append(data['ts'])
-        encoded = encode_command(command, candidate, extend=True)
+        try:
+            encoded = encode_command(command, candidate, extend=extend)
+        except ValueError as error:
+            # Unrepresentable controls invalidate only nearby observations/targets,
+            # not the rest of a recording. Never turn them into negative labels.
+            request = requests.get(data.get('requestId'))
+            unsupported_times.extend([data['ts'], request['ts'] if request else data['ts']])
+            key = json.dumps(command, sort_keys=True)
+            if key not in unsupported and len(unsupported) < 32:
+                unsupported[key] = {'command': command, 'reason': str(error), 'count': 0}
+            if key in unsupported:
+                unsupported[key]['count'] += 1
+            continue
         if encoded is None:
             continue
         stamp = data['ts']
@@ -95,24 +108,25 @@ def prepare(directory, schema, stats):
             # Server safety overrides belong in history, never demonstration
             # targets. Exclude windows around them rather than teaching inaction.
             boundaries.append(stamp)
-    if not targets:
+    stats['unsupportedCommands'] = list(unsupported.values())
+    stats['unsupportedCommandEvents'] = len(unsupported_times) // 2
+    if not targets and not unsupported_times:
         raise ValueError('No accepted client operating commands')
     schema[:] = candidate
-    return metadata, sensors, sorted(history, key=lambda x: x[0]), sorted(targets, key=lambda x: x[0]), sorted(boundaries)
+    return metadata, sensors, sorted(history, key=lambda x: x[0]), sorted(targets, key=lambda x: x[0]), sorted(boundaries), sorted(unsupported_times)
 
 
-def windows(directory, schema, cancelled=lambda: False, stats=None):
+def windows(directory, schema, cancelled=lambda: False, stats=None, extend=True):
     directory = Path(directory)
     stats = stats if stats is not None else {}
-    metadata, sensors, history, targets, boundaries = prepare(directory, schema, stats)
+    metadata, sensors, history, targets, boundaries, unsupported_times = prepare(directory, schema, stats, extend)
     stats.update({'usableWindows': 0, 'commandWindows': 0, 'stopWindows': 0, 'noCommandWindows': 0,
                   'staleSensorWindows': 0, 'boundaryWindows': 0,
-                  'ambiguousWindows': 0, 'videoFrames': 0, 'maxVideoGapMs': 0})
+                  'unsupportedWindows': 0, 'ambiguousWindows': 0, 'videoFrames': 0, 'maxVideoGapMs': 0})
     sensor_times = [row[0] for row in sensors]
     target_times = [row[0] for row in targets]
     available = np.zeros(SLOTS, dtype=np.float32)
-    for _, slot, _ in history:
-        available[slot] = 1
+    available[:len(schema)] = 1
     command_values = np.zeros((SLOTS, VALUES), dtype=np.float32)
     command_times = np.full(SLOTS, -np.inf)
     cursor = 0
@@ -172,6 +186,9 @@ def windows(directory, schema, cancelled=lambda: False, stats=None):
             chosen.append(options[-1])
         if len(chosen) != 4 or stamp + 100 > metadata['endedAt']:
             continue
+        if bisect.bisect_right(unsupported_times, stamp + 100) > bisect.bisect_left(unsupported_times, chosen[0][0]):
+            stats['unsupportedWindows'] += 1
+            continue
         if bisect.bisect_right(boundaries, stamp + 100) > bisect.bisect_left(boundaries, chosen[0][0]):
             stats['boundaryWindows'] += 1
             continue

server/src/services/roverLearningService/workers/policy.py

@@ -4,6 +4,8 @@ Slots describe command shapes, not arbitrary executable instructions. Numeric
 leaves are regressed; string/bool/list values remain exact template constants.
 No command is sent by this module. Execution must use server authorization.
 """
+import base64
+import itertools
 import copy
 import json
 import math
@@ -12,12 +14,12 @@ import numpy as np
 import torch
 from torch import nn
 
-VERSION = 3
+VERSION = 4
 SLOTS = 64
 VALUES = 8
 FRAMES = 4
 HEIGHT, WIDTH = 120, 160
-OPERATING_TYPES = {"drive", "motors", "servo", "headlight", "laser", "horn", "peripheral", "song", "raw"}
+OPERATING_TYPES = {"drive", "motors", "servo", "headlight", "laser", "horn", "song", "raw"}
 SENSORS = [
     ("bumpsAndWheelDrops.bumpLeft", 1), ("bumpsAndWheelDrops.bumpRight", 1),
     ("bumpsAndWheelDrops.wheelDropLeft", 1), ("bumpsAndWheelDrops.wheelDropRight", 1),
@@ -59,18 +61,20 @@ def describe_command(command):
     values, fields = [], []
 
     def visit(value, path=()):
+        if isinstance(value, list) and command['type'] == 'horn' and path == ('horn', 'freqs'):
+            return [visit(item, (*path, index)) for index, item in enumerate(value)]
         if isinstance(value, dict):
             return {key: visit(item, (*path, key)) for key, item in sorted(value.items())}
-        # Exact zeros preserve demonstrated releases/stops. Identity fields must
-        # also stay exact: regression must never invent a peripheral address.
-        if value == 0 or (path and path[-1] in ('id', 'control')):
+        # Exact actuator zeros preserve demonstrated releases. Horn frequency
+        # zeros are parameters so changing a chord does not require a new slot.
+        if value == 0 and command['type'] != 'horn':
             return copy.deepcopy(value)
         if isinstance(value, (int, float)) and not isinstance(value, bool):
             if not math.isfinite(value) or len(values) >= VALUES:
                 raise ValueError('Nonfinite command or too many numeric control fields')
-            scale = {'drive': 500, 'motors': 255, 'servo': 180, 'peripheral': 255,
+            scale = {'drive': 500, 'motors': 255, 'servo': 180,
                      'horn': 1000, 'song': 127}.get(command['type'], 1)
-            fields.append({'path': list(path), 'scale': scale, 'integer': isinstance(value, int)})
+            fields.append({'path': list(path), 'scale': scale, 'integer': command['type'] in ('drive', 'motors', 'servo', 'horn') or isinstance(value, int)})
             values.append(normalize(value, scale))
             return {'$number': len(values) - 1}
         # Arrays (e.g. songs) are discrete exact demonstrations, not variable
@@ -92,7 +96,7 @@ def encode_command(command, schema, extend=False):
         if not extend:
             raise ValueError('Command shape missing from model schema')
         if len(schema) >= SLOTS:
-            raise ValueError('64 command shapes exhausted; recording skipped, not silently truncated')
+            raise ValueError('64 command shapes exhausted; affected windows excluded')
         slot = len(schema)
         schema.append(descriptor)
     vector = np.zeros(VALUES, dtype=np.float32)
@@ -100,10 +104,32 @@ def encode_command(command, schema, extend=False):
     return slot, vector
 
 
+def initial_schema():
+    # Match commandPipeline.js envelopes, OI_COMMANDS and the four-frequency
+    # horn control. Values are examples describing fields, not training labels.
+    commands = []
+    for left, right in itertools.product((0, 1), repeat=2):
+        commands.append({'type': 'drive', 'driveDirect': {'left': left, 'right': right}})
+    for main, side, vacuum in itertools.product((0, 1), repeat=3):
+        commands.append({'type': 'motors', 'motorPwm': {'main': main, 'side': side, 'vacuum': vacuum}})
+    for angle in (0, 1):
+        commands.append({'type': 'servo', 'servo': {'angle': angle}})
+    for kind in ('headlight', 'laser'):
+        for action in ('on', 'off', 'toggle'):
+            commands.append({'type': kind, kind: {'action': action}})
+    for waveform in ('sine', 'saw'):
+        commands.append({'type': 'horn', 'horn': {'action': 'start', 'waveform': waveform, 'freqs': [1, 1, 1, 1]}})
+    commands.append({'type': 'horn', 'horn': {'action': 'stop'}})
+    for opcode in (128, 131, 132, 143):
+        commands.append({'type': 'raw', 'raw': base64.b64encode(bytes([opcode])).decode()})
+    commands.append({'type': 'song', 'song': {'notes': [{'note': 60, 'duration': 8}]}})
+    schema = []
+    for command in commands:
+        encode_command(command, schema, extend=True)
+    return schema
+
+
 def command_family(command):
-    if command['type'] == 'peripheral':
-        peripheral = command.get('peripheral') or {}
-        return ('peripheral', str(peripheral.get('id')), str(peripheral.get('control')))
     return (command['type'],)
 
 
@@ -162,7 +188,7 @@ class Policy(nn.Module):
 
 
 def specification(schema):
-    return {'version': VERSION, 'architecture': 'spatial-cnn-gru-events-v3',
+    return {'version': VERSION, 'architecture': 'spatial-cnn-gru-events-v4',
             'frames': FRAMES, 'height': HEIGHT, 'width': WIDTH,
             'historyStrideMs': 300, 'decisionIntervalMs': 100,
             'stateSize': STATE_SIZE, 'sensorFields': SENSORS,

server/src/services/roverLearningService/workers/train.py

@@ -41,11 +41,11 @@ def main():
     import torch
     from torch.nn import functional as functional
     from dataset import windows
-    from policy import Policy, VERSION, SLOTS, VALUES, specification, is_stop, select_slots
+    from policy import Policy, VERSION, SLOTS, VALUES, specification, is_stop, select_slots, initial_schema
 
     if args.check_dependencies:
         emit('dependencies', torch=str(torch.__version__), numpy=str(np.__version__), av=str(av.__version__),
-             parameters=sum(p.numel() for p in Policy().parameters()), specification=specification([]))
+             parameters=sum(p.numel() for p in Policy().parameters()), specification=specification(initial_schema()))
         return
     if not args.job:
         parser.error('--job is required unless --check-dependencies is used')
@@ -72,8 +72,8 @@ def main():
     optimizer = torch.optim.AdamW(model.parameters(), lr=0.0001)
     # The command-event policy has different inputs, targets and weights. Keep its
     # optimizer lineage separate; reuse recordings with their unchanged partition.
-    resume_path = root / 'training' / 'resume-v4.pt'
-    schema, steps, examples, last_published = [], 0, 0, 0
+    resume_path = root / 'training' / 'resume-v5.pt'
+    schema, steps, examples, last_published = initial_schema(), 0, 0, 0
     trained_rovers = set()
     control_profiles = []
     distinct_windows = {}
@@ -113,7 +113,7 @@ def main():
         held_out = is_held_out(session_id)
         try:
             directory = root / 'recordings' / session_id
-            for sample in windows(directory, candidate_schema, cancelled, stats):
+            for sample in windows(directory, candidate_schema, cancelled, stats, extend=not held_out):
                 seen += 1
                 # Keep evaluation naturally distributed. Training reserves space
                 # for recorded commands and releases so periods without commands
@@ -137,15 +137,15 @@ def main():
             if cancelled():
                 break  # Do not call a partially decoded recording validated.
             if not samples:
+                if held_out and stats.get('unsupportedCommandEvents'):
+                    dataset_stats.append({**stats, 'partition': 'heldOut', 'sampledWindows': 0,
+                                          'skipReason': 'No supported aligned windows'})
+                    continue
                 raise ValueError('No complete, aligned observation/action windows')
             # Entire sessions stay in one partition across all jobs. Neighboring
             # video windows must never straddle training and validation.
             held_out = is_held_out(session_id)
             if held_out:
-                if candidate_schema != schema:
-                    dataset_stats.append({**stats, 'partition': 'heldOut', 'sampledWindows': len(samples),
-                                          'skipReason': 'Vocabulary absent from training data'})
-                    continue  # Retry after training data introduces these command shapes.
                 validation.extend(samples)
             else:
                 schema = candidate_schema
@@ -219,7 +219,12 @@ def main():
     evaluation_started = time.monotonic()
     distinct_windows.update(observed_windows)
     unique_count = sum(distinct_windows.values())
-    evaluation = {'status': 'waiting for held-out sessions', 'windows': len(validation)}
+    coverage = [row for row in dataset_stats if row.get('partition') == 'heldOut']
+    unsupported_events = sum(row.get('unsupportedCommandEvents', 0) for row in coverage)
+    unsupported_windows = sum(row.get('unsupportedWindows', 0) for row in coverage)
+    evaluation = {'status': 'No supported held-out windows' if unsupported_events and not validation else 'waiting for held-out sessions',
+                  'windows': len(validation), 'unsupportedCommandEvents': unsupported_events,
+                  'unsupportedWindows': unsupported_windows}
     if validation and not cancelled():
         model.eval()
         true_positive = predicted_positive = actual_positive = 0
@@ -276,6 +281,7 @@ def main():
         held_out_sessions = sum(row.get('partition') == 'heldOut' and not row.get('skipReason') for row in dataset_stats)
         gate_reasons = []
         for condition, reason in [
+            (unsupported_events == 0, 'Held-out controls are not fully represented; see unsupportedCommands'),
             (evaluated == len(validation), 'Evaluation interrupted'),
             (held_out_sessions >= 2, 'Need two usable held-out recordings'),
             (evaluated >= 128, 'Need 128 held-out windows'),
@@ -291,6 +297,7 @@ def main():
             if not condition:
                 gate_reasons.append(reason)
         evaluation = {'status': 'imitation gate not met' if gate_reasons else 'passed imitation gate',
+                      'unsupportedCommandEvents': unsupported_events, 'unsupportedWindows': unsupported_windows,
                       'method': 'Held-out 100ms command events; not a closed-loop rollout',
                       'threshold': job['actionThreshold'], 'heldOutSessions': held_out_sessions,
                       'windows': evaluated, 'precision': precision, 'recall': recall,

webui/src/components/vip/VipRoverLearningCard.jsx

@@ -133,7 +133,7 @@ function LearningCard({ roverId }) {
       <div className="flex flex-wrap items-center justify-center gap-1">
         <select aria-label="Rover model" className={fieldClass} value={chosen?.id || ''} onChange={(event) => setSelectedId(event.target.value)} disabled={working || !models.length}>
           {!models.length && <option value="">No published models yet</option>}
-          {models.map((model) => <option key={model.id} value={model.id}>{favorites.includes(model.id) ? '★ ' : ''}{model.name}{model.experimental ? ' (experimental)' : ''}{model.compatible ? '' : model.formatVersion !== 3 ? ' (older model format)' : ' (incompatible)'}</option>)}
+          {models.map((model) => <option key={model.id} value={model.id}>{favorites.includes(model.id) ? '★ ' : ''}{model.name}{model.experimental ? ' (experimental)' : ''}{model.compatible ? '' : model.formatVersion !== 4 ? ' (older model format)' : ' (incompatible)'}</option>)}
         </select>
         <button type="button" className="button-dark text-sm disabled:opacity-50" aria-pressed={Boolean(chosen && favorites.includes(chosen.id))} disabled={!chosen} onClick={favorite}>
           {chosen && favorites.includes(chosen.id) ? '★ Favorite' : '☆ Favorite'}