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Learning Gaps Roadmap

What’s missing, why it matters, and in what order to build it

The Gap Analysis

All seven tracks have been written. The table below shows each track’s current status. Future work: Linux RT internals (extend cpp-advanced/03) and deeper hardware exercises once the STM32 + logic analyzer are in hand.

EXISTING (COMPLETE)                     STATUS
────────────────────────────────────    ────────────────────────────────────────
electronics/    (hardware theory)       🟢 7 lessons + exercises
zephyr/         (firmware + deep-dive)  🟢 17 tutorials + 12 deep-dives
cpp-advanced/   (C++ depth)             🟢 18 modules + exercises
control-systems/ (PID → advanced)       🟢 10 lessons + exercises
navigation-estimator/ (EKF/IMU)         🟢 6 lessons + 5 exercises
ros2-handson/   (nodes/TF/Nav2)         🟢 3 lessons + 4 exercises
python-scripting/ (typing/test/polars)  🟢 3 lessons + 3 exercises

Track 1: Navigation & State Estimation

Priority: HIGHEST Why now: You investigate ESTIMATOR_STATE_INVALID, slip detection, line-sensor covariance blow-up, and EKF divergence tickets daily. You know the robot code deeply (repo memory) but lack the mathematical theory to immediately see why a covariance grows, explodes, or converges.

Goal: Be able to look at any estimator log and reason from first principles about whether the observed covariance trajectory is expected or anomalous.

Timeline: 6 weeks · 3–4 hrs/week · no hardware needed

Status: 🟢 Complete — 6 lessons + 5 exercises written Folder: learn/navigation-estimator/

Week Topic Practical Relevance
1 Dead-reckoning & wheel odometry math predict() noise model
2 EKF theory: prediction step, covariance propagation Covariance INF = slipped/collision
3 EKF measurement update: Mahalanobis, innovation gating Line-Sensor update in update()
4 IMU fusion, gyro bias, line-sensor as line-constraint measurement Theta update + gyro correction
5 Diagnosing estimator failures from logs/bags Apply to real tickets
6 Ceres line-sensor solver internals and failure diagnosis Bell-curve fit failures and estimator-side debugging

Files to create:

navigation-estimator/
├── 00-learning-plan.md        ← dependency graph, study order, checkpoints
├── 01-dead-reckoning.md       ← odometry math, unicycle model, arc integration
├── 02-kalman-filter.md        ← KF → EKF derivation, predict/update equations
├── 03-measurement-models.md   ← line sensors as constraints, Mahalanobis gating
├── 04-imu-fusion.md           ← gyro integration, bias estimation, line-sensor theta
├── 05-failure-modes.md        ← covariance blow-up patterns, slip vs collision vs delocalize
├── 06-ceres-line-sensor-deep-dive.md ← line-fit solver behavior, fit diagnostics, edge cases
└── exercises/
    ├── 01-odometry-math.md
    ├── 02-kalman-1d.md        ← compute 1D Kalman predict and update steps with explicit numbers
    ├── 03-ekf-unicycle.md     ← work through line constraints, innovations, and measurement rejection by hand
    ├── 04-log-diagnosis.md    ← reason about gyro bias, theta fusion, and when IMU corrections should be trusted
    └── 05-robot-specific.md   ← read estimator symptoms like RCA cases and separate slip, delocalization, and collision paths

Track 2: ROS2 Hands-On Exercises

Priority: HIGH Why: zephyr/study-notes/05-jetson-ros2.md (1524 lines) is theory-only. You work with ROS2 topics, nodes, tf2, nav2 every day but haven’t built a systematic exercise set.

Goal: Be able to write, debug, and tune any ROS2 node from scratch. Understand DDS/QoS well enough to explain frame drop bugs.

Timeline: 3 weeks · 3–4 hrs/week

Status: 🟢 Complete — 3 lessons + 4 exercises written Folder: learn/ros2-handson/

Week Topic Practical Relevance
1 Nodes, topics, services, actions, lifecycle AMR node architecture
2 tf2 transforms, time sync, QoS profiles TF lookup failures in nav logs
3 Nav2 BT architecture, costmaps, planner/controller RCS navigation stack

Files to create:

ros2-handson/
├── 00-learning-plan.md
├── 01-nodes-topics-actions.md
├── 02-tf2-time-qos.md
├── 03-nav2-architecture.md
└── exercises/
    ├── 01-pub-sub-timer.md
    ├── 02-tf2-broadcaster.md
    ├── 03-action-server.md
    └── 04-nav2-diagnostics.md

Track 3: Python / Robot Scripting

Priority: MEDIUM Why: You write Python scripts daily (RCA analysis, KB tools, log parsing) but there’s no structured skill-building. Gaps: async patterns, dataclass typing, pandas/polars for log analysis, and test coverage for scripts.

Goal: Write analysis scripts that are testable, typed, and maintainable, not just “it works on my machine.”

Timeline: 3 weeks · 2–3 hrs/week

Status: 🟢 Complete — 3 lessons + 3 exercises written Folder: learn/python-scripting/

Week Topic Practical Relevance
1 Type hints, dataclasses, Pydantic for config validation session_tracker.py, knowledge_search.py
2 pytest, fixtures, mocking for CLI scripts Script tests in tests/
3 pandas/polars for time-series log analysis Bag CSV analysis patterns

Files to create:

python-scripting/
├── 00-learning-plan.md
├── 01-types-dataclasses.md
├── 02-testing-cli-scripts.md
├── 03-timeseries-analysis.md
└── exercises/
    ├── 01-type-annotations.py
    ├── 02-pytest-fixtures.py
    └── 03-log-analysis-polars.py

Track 4: Zephyr Deep-Dive

Priority: MEDIUM (blocked by hardware) Why: zephyr/deep-dive/ now has a complete written pass. Future additions can be layered in once the STM32 + logic analyzer are in hand for hardware-backed validation.

Timeline: Add content incrementally as hardware is acquired (see zephyr/00-mastery-plan.md hardware list)

Status: 🟢 Complete — 12 deep-dives + README written Folder: learn/zephyr/deep-dive/

Files to create (when hardware arrives):

zephyr/deep-dive/
├── 01-first-build-flash-debug.md   ← blinky to shell, GDB, Segger RTT
├── 02-spi-slave-first-frame.md     ← wire up logic analyzer, capture first byte
├── 03-dma-cache-gotchas.md         ← reproduce the D-cache coherency bug on real HW
├── 04-imu-i2c-reads.md             ← ICM-42688 register read via Zephyr I2C API
└── 05-100hz-spi-bridge.md          ← the full capstone

Track 5: Linux RT Internals

Priority: LOW Why: cpp-advanced/03-rt-linux-programming partially covers this (SCHED_FIFO, mutex, priority inversion). Not a separate track — extend cpp-advanced/03 instead.

Status: 🟢 Partially covered in cpp-advanced/03-rt-linux-programming/

Action: After completing cpp-advanced weeks 1–5, add:

  • perf and ftrace for latency profiling
  • /proc/latency_stats interpretation
  • PREEMPT_RT patch model

All content is written. Suggested study order for the best learning flow:

CORE (do these first, in parallel):
│
├── navigation-estimator/   ← highest RCA value, 6 weeks
├── cpp-advanced/           ← 18 modules, work through progressively
│
THEN:
│
├── ros2-handson/           ← builds on navigation-estimator theory
├── python-scripting/       ← can interleave with anything
│
ALWAYS AVAILABLE:
│
├── electronics/            ← reference as needed
├── control-systems/        ← PID → advanced control theory
└── zephyr/                 ← firmware + 12 deep-dives

Quick-Start: Next Session

Start here → navigation-estimator/00-learning-plan.html

Day 1 goal (2 hrs): Read 01-dead-reckoning.md and be able to derive (x', y', θ') from two encoder counts for a differential-drive robot.

This directly ties to predict() in navigation estimator — you’ll recognize every variable.