Gemini 3.0 challenge: Stop building things that walk and start building things to fly. The solution is Neuro-Symbolic AI. The codebase from rigid-body drones to articulated robot dogs usually implies a rewrite.Gemini 3.0 challenge: Stop building things that walk and start building things to fly. The solution is Neuro-Symbolic AI. The codebase from rigid-body drones to articulated robot dogs usually implies a rewrite.

From Drones to Robot Dogs: How I Refactored a Manufacturing Engine in 88k Tokens

2025/12/02 12:45

\ Recently, as part of my Gemini 3.0 challenge, I completed the OpenForge Neuro-Symbolic Manufacturing Engine. The system successfully designed, sourced, and simulated custom drones. But I wanted to push the model further. I wanted to see if the architecture was brittle (overfit to drones) or robust (capable of general engineering).

So, I gave the system a new directive: Stop building things that fly. Start building things that walk.

Incredibly, within just 88,816 tokens of context and code generation, the system pivoted. It stopped looking for Kv ratings and propellers and started calculating servo torque and inverse kinematics.

Here is how I used Gemini not as a Source of Truth, but as a logic translator to engineer the Ranch Dog.

The Fatal Flaw: The LLM as a Database

In my previous article I discussed the fatal flaw in most AI engineering projects: treating the Large Language Model (LLM) as a database of facts. If you ask an LLM, Design me a drone, it hallucinates. It suggests parts that don't fit, batteries that are too heavy, or motors that don't exist.

The solution is Neuro-Symbolic AI.

  • Neural (The LLM): Used for Translation. It translates user intent: I need a robot to carry feed bags into mathematical constraints (Payload > 10kg).
  • Symbolic (The Code): Used for Truth. Python scripts calculate the physics, verify the voltage compatibility, and generate the CAD files.

The LLM never calculates. It only configures the calculator.

The Pivot: From Aerodynamics to Kinematics

Refactoring a codebase from rigid-body drones to articulated robot dogs usually implies a rewrite. However, because of the Neuro-Symbolic architecture, the skeleton of the code remained the same. I only had to swap the organs.

Here is how the architecture handled the pivot:

1. The Brain Transplant (Prompts)

The first step was retraining the agents via prompts. I didn't change the Python service that runs the logic; I just changed the instructions Gemini uses to select the logic.

I updated prompts.py to remove aerodynamic axioms and replace them with kinematic ones. The system immediately stopped caring about Hover Throttle and started optimizing for Stall Torque:

# app/prompts.py REQUIREMENTS_SYSTEM_INSTRUCTION = """ You are the "Chief Robotics Engineer". Translate user requests into QUADRUPED TOPOLOGY. KNOWLEDGE BASE (AXIOMS): - "Heavy Haul" / "Mule": Requires High-Torque Serial Bus Servos (30kg+), shorter femurs. - "Fence Inspector": Requires High-Endurance, Lidar/Camera mast. - "Swamp/Mud": Requires sealed actuators (IP-rated), wide footpads. OUTPUT SCHEMA (JSON ONLY): { "topology": { "class": "String (e.g., Heavy Spot-Clone)", "target_payload_kg": "Float", "leg_dof": "Integer (usually 3 per leg)" }, "technical_constraints": { "actuator_type": "String (e.g., Serial Bus Servo)", "min_torque_kgcm": "Float", "chassis_material": "String" } } """

2. The Sourcing Pivot (Data Ingestion)

This was the most critical test. The system's Fusion Service scrapes the web for real parts. The scraper remained untouched, but I updated the Library Service to identify servos instead of brushless motors.

Instead of regex matching for Kv ratings, library_service.py now identifies whether a servo is a cheap toy (PWM) or a robotics-grade component (Serial Bus): \n

# app/services/library_service.py STANDARD_SERVO_PATTERNS = { # Micro / Hobby (PWM) "SG90": {"torque": 1.6, "type": "PWM", "class": "Micro"}, # Robotics Serial Bus (The good stuff) "LX-16A": {"torque": 17.0, "type": "Serial", "class": "Standard"}, "XM430": {"torque": 40.0, "type": "Dynamixel", "class": "Standard"}, } def infer_actuator_specs(product_title: str) -> dict: # Logic to infer torque if the Vision AI misses it if "est_torque_kgcm" not in specs: match = re.search(r"\b(\d{1,3}(?:\.\d)?)\s?(?:kg|kg\.cm)\b", title_lower) if match: specs["est_torque_kgcm"] = float(match.group(1)) return specs

3. The Physics Pivot (Validation)

In the drone build, physics_service.py calculated Thrust-to-Weight ratios. For the robot dog, Gemini rewrote this service to calculate Static Torque Requirements. It uses lever-arm physics to ensure the servos selected by the Sourcing Agent can actually lift the robot.

# app/services/physics_service.py def _calculate_torque_requirements(total_mass_kg, femur_length_mm): """ Calculates the minimum torque required to stand/trot. Torque = Force * Distance. """ # Force per leg (2 legs supporting body in trot gait) force_newtons = (total_mass_kg * GRAVITY) / 2.0 # Distance = Horizontal projection of the Femur lever_arm_cm = femur_length_mm / 10.0 required_torque_kgcm = (total_mass_kg / 2.0) * lever_arm_cm return required_torque_kgcm

4. The Simulation Pivot (Isaac Sim)

In NVIDIA Isaac Sim, a drone is a simple Rigid Body. A Quadruped is an Articulation Tree of parents and children connected by joints.

I tasked Gemini with rewriting isaac_service.py. It successfully swapped RigidPrimView for ArticulationView and implemented stiffness damping to simulate servo holding strength:

# app/services/isaac_service.py def generate_robot_usd(self, robot_data): # CRITICAL: Apply Articulation Root API # This tells Isaac Sim "Treat everything below this as a system of joints" UsdPhysics.ArticulationRootAPI.Apply(root_prim.GetPrim()) # Define The Joint (Revolute) representing the Servo self._add_revolute_joint( stage, parent_path=chassis_path, child_path=femur_path, axis="y", # Rotates around Y axis (swing) stiffness=10000.0 # High stiffness = Strong Servo )

5. The Locomotion Pivot (Inverse Kinematics)

Drones rely on PID controllers to stay level. Dogs require Inverse Kinematics (IK) to figure out how to move a foot to coordinates 

(x,y,z)(x,y,z)

Gemini generated a new 2-DOF planar IK solver (ik_service.py) that uses the Law of Cosines to calculate the exact angle the hip and knee servos need to hold to keep the robot standing.

# app/services/ik_service.py def solve_2dof(self, target_x, target_z): # Law of Cosines to find knee angle cos_knee = (self.l1**2 + self.l2**2 - r**2) / (2 * self.l1 * self.l2) alpha_knee = math.acos(cos_knee) # Calculate servo angle knee_angle = -(math.pi - alpha_knee) return hip_angle, knee_angle

The Result: 88,816 Tokens Later

The resulting system, OpenForge, is now a dual-threat engine. It can take a persona-based request: I am a rancher and I need a robot to patrol my fence line" and autonomously:

  1. Architect a high-endurance quadruped topology.
  2. Source real Lidar modules and long-range servos from the web.
  3. Validate that the battery voltage matches the servos (preventing magic smoke).
  4. Generate the CAD files for the chassis and legs.
  5. Simulate the robot walking in a physics-accurate environment.

This pivot wasn't about the robot. It was about the Agility of Neuro-Symbolic Architectures as well as Gemini 3.0. By decoupling the Reasoning (LLM) from the Execution (Code), you can refactor complex systems at the speed of thought.

\ This article is part of my ongoing Gemini 3.0 challenge to push the boundaries of automated engineering.

\

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By Nancy, PANews News that Tether is in talks to raise funds at a $500 billion valuation has propelled it to new heights. If the deal goes through, its valuation would leap to the highest of any global crypto company, rivaling even Silicon Valley unicorns like OpenAI and SpaceX. Tether, with its strong capital base, boasts profit levels that have driven its price-to-earnings ratio beyond the reach of both crypto and traditional institutions. Yet, its pursuit of a new round of capital injection at a high valuation serves not only as a powerful testament to its profitability but also as a means of shaping the market narrative through capital operations, building momentum for future business and market expansion. Net worth soared more than 40 times in a year, and well-known core investors are being evaluated. On September 24, Bloomberg reported that stablecoin giant Tether is planning to sell approximately 3% of its shares at a valuation of $15 billion to $20 billion. If the deal goes through, Tether's valuation could reach approximately $500 billion, making it one of the world's most valuable private companies and potentially setting a record for the largest single financing in the history of the crypto industry. By comparison, in November 2024, Cantor Fitzgerald, a prominent US financial services firm, acquired approximately 5% of Tether for $600 million, valuing the company at approximately $12 billion. This means Tether's value has increased more than 40-fold in less than a year. However, since Cantor Fitzgerald's former CEO, Howard Lutnick, is currently the US Secretary of Commerce, the deal was interpreted as a "friendship price" that could potentially garner more political support for Tether. Tether's rapid rise in value is largely due to its dominant market share, impressive profit margins, and solid financial position. According to Coingecko data, as of September 24th, USDT's market capitalization exceeded $172 billion, setting a new record and accounting for over 60% of the market share. Furthermore, Tether CEO Paolo Ardoino recently admitted that Tether's profit margin is as high as 99%. The second-quarter financial report further demonstrates Tether's robust financial position, with $162.5 billion in reserve assets exceeding $157.1 billion in liabilities. "Tether has about $5.5 billion in cash, Bitcoin and equity assets on its balance sheet. If calculated based on the approximately $173 billion USDT in circulation and a 4% compound yield, and if it raises funds at a valuation of $500 billion, it means that its enterprise value to annualized return (PE) multiple is about 68 times," Dragonfly investor Omar pointed out. Sources familiar with the matter revealed that the disclosed valuation represents the upper end of the target range, and the final transaction value could be significantly lower. Negotiations are at an early stage, and investment details are subject to change. The transaction involves the issuance of new shares, not the sale of shares by existing investors. Paolo Ardoino later confirmed that the company is actively evaluating the possibility of raising capital from a number of prominent core investors. Behind the high valuation of external financing, the focus is on business expansion and compliance layout Tether has always been known to be "rich." The stablecoin giant is expected to generate $13.7 billion in net profit in 2024, thanks to interest income from U.S. Treasury bonds and cash assets. For any technology or financial company, this profit level is more than enough to support continued expansion. However, Tether is now launching a highly valued external financing plan. This is not only a capital operation strategy, but also relates to business expansion and regulatory compliance. According to Paolo Ardoino, Tether plans to raise funds to expand the company's strategic scale in existing and new business lines (stablecoins, distribution coverage, artificial intelligence, commodity trading, energy, communications, and media) by several orders of magnitude. He disclosed in July this year that Tether has invested in over 120 companies to date, and this number is expected to grow significantly in the coming months and years, with a focus on key areas such as payment infrastructure, renewable energy, Bitcoin, agriculture, artificial intelligence, and tokenization. In other words, Tether is trying to transform passive income that depends on the interest rate environment into active growth in cross-industry investments. But pressure is mounting. With the increasing number of competitors and the Federal Reserve resuming its interest rate cut cycle, Tether's main source of profit faces downward risks. 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PANews2025/09/24 15:52