OBI Robotics 與 Ontological Bayesian Intelligence 的 12 週工程路線圖

OBI Robotics × Ontological Bayesian Intelligence

12-week engineering roadmap

OBINexus Computing · Mechanical-first development · C/C++ robotics · 20 hours per week

The objective is to turn OBI Robotics from an architecture and CAD concept into a testable physical prototype, while developing the Ontological Bayesian Intelligence (OBI) as a separate, measurable reasoning system that can eventually operate with the robot.

The engineering principle: design the physical system first, model what it can sense and do, then build intelligence around the actual constraints of that system.

Your uploaded Unbiased_AI.pdf provides the proposed intelligence framework: Bayesian modelling for bias analysis, structural processing, modular AI components and a staged implementation roadmap. We'll use those ideas as the research foundation, but distinguish mathematical methods with established uses from hypotheses in the paper that still require experimental validation.

Unbiased_AI.pdf

The 12-week target

Mechanical

A dimensioned CAD assembly, load calculations and a documented prototype design.

Electrical

A power budget, wiring architecture and verified sensor/motor interfaces.

C/C++ software

Modular drivers, a state-estimation interface and automated tests.

OBI intelligence

An ontology, a Bayesian inference prototype and reproducible evaluation results.

Total engineering allocation

240 hours

Duration

12 weeks

One important scope boundary: the 12-week deliverable is a validated prototype and an integration-ready architecture, not a finished autonomous robot or a newly trained large language model. The exact hardware build depends on your robot's intended function, payload, budget and available equipment.

1. System architecture and design decisions

Use a layered design with explicit interfaces. Keep the physical control loop independent of the AI reasoning layer.

OBI Robotics Application

Tasks · diagnostics · human interface

Ontological Bayesian Intelligence

Ontology · belief state · inference · uncertainty · decision proposals

Verified decision interface

Validate commands · enforce limits · reject invalid actions

Embedded control

C/C++ · motor drivers · feedback

Physical platform

Motors · sensors · power · structure

Decisions to make before building

Decision	Initial recommendation	Why
Robot type	Start with a simple mobile base or single-joint test rig	Limits initial mechanical complexity
CAD approach	Parametric CAD with a dimensioned assembly	Makes revisions traceable
Control hardware	Select a microcontroller based on motor, sensor and timing needs	Avoids premature hardware commitment
Software	C++ for control and reusable components; C where appropriate for low-level interfaces	Supports modular, resource-conscious software
Intelligence	Separate C++ inference interface from the control loop	Enables independent testing
Bayesian prototype	Start with a small, interpretable model	Makes probabilities and failure cases inspectable
LLM	Do not make it a dependency of the first prototype	Keeps the system testable without a generative model

These are provisional engineering decisions, not facts about your existing hardware. Revisit them when you have defined the robot's task and operating environment.

2. The weekly engineering schedule

Use the same 20-hour structure each week, adjusting it when a milestone needs extra attention.

Mechanical engineering and CAD

8 h

Electrical and embedded systems

4 h

C/C++ software and testing

4 h

OBI intelligence and mathematics

4 h

Each week, reserve time to update the design log, commit working code, record test results and identify unresolved risks. Do not count a component as complete merely because its CAD model or source code exists.

3. Twelve-week roadmap

Phase I — Requirements and mechanical foundations

W1

20 hours

Define the robot and its operating requirements

Prerequisites: none; this is your starting point.

Tasks:

Write a one-page mission statement for OBI Robotics.
Define operating environment, intended task, payload, mobility and human interaction.
List inputs, outputs, power needs and safety constraints.
Create a preliminary system block diagram and requirements register.
Define the first ontology concepts: Robot, Component, Sensor, Actuator, Environment and Fault.

Tests and acceptance criteria

Every major subsystem has a defined purpose and interface.
Each requirement has a unique ID and a verification method.
Unknown values are explicitly marked as assumptions.

Deliverables: requirements.md, architecture diagram, risk register and initial ontology.

W2

20 hours

Create the mechanical concept and first CAD assembly

Prerequisites: approved requirements and initial envelope dimensions.

Tasks:

Compare two feasible mechanical layouts.
Select the chassis or joint configuration.
Model the base, mounting plates, actuator locations and sensor mounts.
Define interfaces, fasteners, clearances and service access.
Establish a preliminary bill of materials.

Tests and acceptance criteria

Assembly dimensions match the agreed design envelope.
No unintended part collisions in the CAD assembly.
Components can be assembled and serviced in the proposed sequence.

Deliverables: native CAD assembly, dimensioned drawings, layout comparison and preliminary BOM.

W3

20 hours

Calculate loads and size the actuators

Prerequisites: approximate mass, geometry, payload and movement requirements.

Tasks:

Estimate component and payload masses.
Calculate static loads, moments and required output torque.
For a mobile base, estimate wheel force, traction and slope requirements.
For a jointed robot, estimate gravity torque and acceleration requirements.
Select candidate motors, gear ratios, bearings and structural materials.

Tests and acceptance criteria

Every major load-bearing part has a documented design load.
Motor selection is supported by torque-speed and thermal data, not stall torque alone.
Structural assumptions and safety factors are documented.

Deliverables: engineering calculation sheet, actuator selection report and updated CAD assembly.

Phase II — Mechanical design and electrical architecture

W4

20 hours

Close the mechanical design

Tasks:

Refine mounts, fasteners, bearing supports and cable routing.
Check interference across intended movement.
Identify parts to fabricate and parts to buy.
Perform preliminary structural checks where material data and loads permit.
Freeze the first revision of the assembly.

Tests: dimension checks, joint travel checks, clearance review and independent calculation review.

Deliverable: CAD revision A, manufacturing drawings and a fabrication decision.

W5

20 hours

Design the electrical and power system

Tasks:

Calculate voltage rails, peak and continuous current requirements.
Select a suitable power source, regulators, motor drivers and protection.
Define sensor interfaces, connectors, grounding and cable routing.
Establish the hardware emergency-stop architecture.
Produce an electrical schematic and interface table.

Tests: verify component ratings against expected loads, calculate voltage drop and power dissipation, and review battery protection and fault isolation.

Deliverable: electrical schematic, power budget, wiring plan and preliminary safety review.

W6

20 hours

Build the first C/C++ embedded interfaces

Tasks:

Create a repository and establish the build and test system.
Implement typed interfaces for sensors, actuators and robot state.
Define units, error codes, timestamps and sensor-validity flags.
Add hardware abstraction interfaces so simulation and physical drivers can share contracts.
Implement a basic mock sensor and actuator.

Tests: successful clean build, unit tests for valid and invalid readings, simulated sensor dropout and deterministic actuator-command validation.

Deliverable: compiling C/C++ skeleton, interface specifications and automated tests.

Phase III — Intelligence foundations

W7

20 hours

Formalise the ontology and world state

Tasks:

Define entities, attributes, relationships and units.
Distinguish observed values from inferred states.
Represent component health, operating mode and environmental conditions.
Define how missing, stale or contradictory sensor observations are represented.
Specify the interface between the ontology and Bayesian variables.

Tests: schema validation, invalid relationship rejection, unit consistency and round-trip serialisation tests.

Deliverable: ontology specification, versioned schema and representative robot-state examples.

W8

20 hours

Implement the first Bayesian inference model

Tasks:

Choose one narrow problem, such as estimating motor health.
Define variables, priors, likelihoods and conditional dependencies.
Generate a synthetic dataset with known ground truth.
Implement inference using a small, transparent model.
Compare estimated probabilities against known outcomes.

Tests: probability normalisation, known-case checks, sensitivity to priors, missing-data behaviour and reproducibility.

Deliverable: working inference prototype, model assumptions and test dataset.

W9

20 hours

Integrate sensor state and inference

Tasks:

Connect the sensor abstraction to the ontology.
Pass validated observations to the inference engine.
Maintain timestamps and separate raw measurements from inferred beliefs.
Return estimates, confidence information and data-quality warnings.
Add logs that permit replay of a recorded scenario.

Tests: deterministic replay, stale-sensor rejection, injected sensor faults and comparison with known simulation states.

Deliverable: end-to-end simulated perception and inference pipeline.

Phase IV — Verification and integration

W10

20 hours

Test uncertainty and bias-related claims

Tasks:

Generate balanced and deliberately shifted synthetic datasets.
Test whether inference is calibrated on held-out cases.
For any fairness-relevant use case, define the relevant groups, outcome, comparison and acceptable disparity before testing.
Examine how priors, confounders and missing variables affect conclusions.
Record where the model cannot make a reliable inference.

Tests: calibration curves or Brier score, false-positive and false-negative rates, sensitivity analysis and documented limitations.

Deliverable: evaluation report with numerical results, failure cases and reproducible experiments.

W11

20 hours

Connect inference to safe decision proposals

Tasks:

Define a narrow action interface, such as proposing reduced motor speed after a suspected fault.
Validate every proposal against deterministic constraints.
Add a human override and emergency-stop path.
Test timeouts, invalid outputs, sensor failures and controller restarts.
Keep AI inference outside any hard real-time control loop.

Tests: invalid-action rejection, fail-safe response to missing data, controller independence and fault-injection testing.

Deliverable: a safety-gated decision interface with documented test results.

W12

20 hours

Demonstrate, audit and plan revision B

Tasks:

Assemble the prototype if hardware and fabrication are ready; otherwise demonstrate the validated simulation.
Run a repeatable demonstration scenario.
Measure performance against the week-one requirements.
Review mechanical, electrical, software and inference failure modes.
Produce a revision plan based on test evidence rather than assumptions.

Tests: repeat the same scenario multiple times, preserve logs and confirm all critical requirements have a test result.

Deliverable: demonstration, design dossier, test report, known-limitations register and next-phase plan.

4. Grounding the intelligence in your Unbiased AI paper

Your paper should remain the conceptual foundation for the OBI intelligence work. Here is how I would translate its actual proposals into experiments.

Paper concept	Engineering implementation	Evidence required
Bias as learned patterns	Controlled experiments with known data distributions	Show how predictions change when the data distribution changes
Bayesian network for bias analysis	Explicit variables and conditional probability tables or distributions	Check assumptions and compare inference with known synthetic ground truth
Structural unboxing of high-dimensional data	Build a defined transformation from sensor arrays into a semantic state representation	Validate information preservation and downstream task performance
Modular AI architecture	Separate ontology, inference, perception and interface modules	Independent unit tests and integration tests
Marginalisation over bias variables	Compute or approximate posterior distributions with nuisance variables	Compare numerical results with an analytically solvable or independently verified reference
Equity and transparency outcomes	Define task-specific fairness and interpretability measures	Report actual measured outcomes, uncertainty and limitations

These are proposed implementations and experiments, not claims that the paper has already demonstrated working bias removal or complete transparency.

Unbiased_AI.pdf

An important mathematical distinction

The marginalisation equation in the paper,

\[ P(\theta\mid D)=\int P(\theta,\phi\mid D)\,d\phi, \]

is a valid probability identity when the joint posterior and integration are well-defined. But marginalising over a bias variable does not, by itself, guarantee an unbiased model. That depends on the model, the data, the meaning of the variables and the target criterion.

Likewise, the paper's suggested conversion of high-dimensional tensor data into semantic maps needs a specific algorithm, a defined representation and an evaluation benchmark before its effectiveness can be established.

For OBI, I would keep two experimental tracks separate:

Physical intelligence: Can the model infer robot state and faults reliably?
Equity intelligence: Can explicit modelling improve a specified fairness outcome without hiding uncertainty or degrading other important metrics?

This makes the results more defensible and avoids claiming that success on one task proves success on the other.

5. Proposed C/C++ project structure

Start with a small, testable codebase rather than a monolithic AI program.

obi-robotics/ ├── docs/ │ ├── requirements.md │ ├── architecture.md │ ├── safety-case.md │ └── test-plan.md ├── mechanical/ │ ├── cad/ │ ├── drawings/ │ └── calculations/ ├── electrical/ │ ├── schematics/ │ ├── power-budget.md │ └── interfaces.md ├── src/ │ ├── hardware/ │ ├── sensors/ │ ├── actuators/ │ ├── state/ │ ├── ontology/ │ ├── bayesian/ │ ├── decision/ │ └── diagnostics/ ├── tests/ │ ├── unit/ │ ├── integration/ │ ├── simulation/ │ └── fault_injection/ ├── data/ │ ├── synthetic/ │ └── evaluation/ ├── CMakeLists.txt └── README.md

Suggested interfaces:

struct SensorReading {
    double value;
    double timestamp_seconds;
    bool valid;
};

struct BeliefEstimate {
    double probability;
    bool valid;
};

class StateEstimator {
public:
    virtual ~StateEstimator() = default;

    virtual BeliefEstimate estimate(
        const SensorReading& reading
    ) = 0;
};

This is a starting interface, not a complete Bayesian implementation. In production, use explicit units, typed fault states, validated timestamps, defined probability semantics and appropriate synchronisation. The inference interface should eventually accept the full observation set and return a distribution over named hypotheses, not just a single probability.

6. Measurable completion criteria

At the end of 12 weeks, score the project against evidence rather than effort.

Engineering acceptance checklist

0/8

Requirements and architecture

All critical requirements have IDs, owners and verification methods.

Mechanical design

Dimensioned CAD assembly, interference checks and documented load calculations.

Electrical design

Reviewed schematic, power budget, connector definitions and protection strategy.

C/C++ code

Reproducible build and automated tests for defined interfaces.

Bayesian inference

Known-case tests pass and posterior probabilities are valid.

Evaluation

Held-out data, calibration results and limitations are documented.

Safety

Invalid actions and defined fault scenarios are rejected or handled safely.

Demonstration

A repeatable simulation or physical prototype test with preserved logs.

Copy checklist

This checklist is an acceptance framework, not a claim that any of the work has already been completed.

7. What I would deliberately postpone

Training a language model from scratch.
Building custom hardware for every sensor and actuator before proving the architecture.
Complex autonomous navigation before basic state estimation and control are reliable.
Claims that Bayesian modelling automatically eliminates bias.
Adding voice, vision and other modules before the first end-to-end use case works.

You can later introduce an existing LLM as a language interface, while retaining the ontology, probabilistic inference and safety checks as separately testable components.

Your first task: Week 1

Start with the mission statement and system requirements. Do not begin by modelling an attractive robot chassis before defining what it must physically accomplish.

There is one crucial decision that will determine the rest of the mechanical design: what is OBI Robotics meant to do first?

Is your initial target a mobile robot, a robotic arm or manipulator, a human-assistance robot, or a different machine? That choice determines the loads, actuators, geometry, sensors, electrical budget and the first Bayesian model worth building.