Embodied AI

Objective

To spearhead the complete research and development lifecycle for a novel, on-device artificial intelligence system for high-precision state estimation on a mobile robotics platform. From an ambiguous high-level goal, a fully-deployed system was delivered.

Challenges

• Severe Hardware Constraints: The platform’s on-board sensors are resource-constrained and provide low-accuracy, high-noise data.
• SOTA Benchmarking & Failure: I conducted a comprehensive literature review, implementing and benchmarking numerous state-of-the-art (SOTA) algorithms. These methods failed, yielding high errors.
• Root Cause Analysis: The SOTA methods were fundamentally unsuited for our problem, as they are designed for high-fidelity sensors, not noisy data, and optimized for stationary receivers, whereas our platform is highly dynamic and mobile.

Methodology & Solution

• Literature Review & Benchmarking: Conducted a comprehensive review of SOTA methodologies for state estimation. Key SOTA algorithms were implemented and benchmarked on the platform’s hardware. Given the failure of existing methods, I devised a custom, hardware-aware algorithm from first principles.

• Novel Algorithm Design (Manifold Disentanglement): The core innovation is a novel manifold disentanglement technique. Instead of solving the high-dimensional state estimation problem directly (which is highly sensitive to sensor noise), my algorithm first reframes the problem onto a robust, lower-dimensional manifold. This intermediate step is robust to noise and its output is then used to solve the full state estimation problem.

• Dynamic Platform Accommodation: The algorithm was designed from the ground up to explicitly account for a dynamic, mobile platform, a key differentiator from most SOTA methods.

• Prototyping & Validation: I developed initial prototypes in Python to validate the theory against simulated data. After success, I built a robust data pipeline to ingest, process, and replay historical telemetry data from numerous real-world operational runs, enabling massive-scale regression testing. This pipeline included a robotic simulation & analysis platform.

• Production Implementation: The final, high-performance algorithm was implemented in Lisp. This system was rigorously validated through a multi-stage pipeline, progressing from high-fidelity simulation to extensive on-hardware performance analysis to prove its real-world robustness.

Technology Stack

• Lisp: Used for the core, on-device, high-performance algorithm. Selected for its high-speed execution, low-level customizability, and powerful metaprogramming capabilities, which enabled rapid debugging and iterative development directly on the running system.
• Python: Used for data pipelines, simulation, benchmarking, prototyping, and validation.

Results & Impact

• Performance: The custom algorithm achieved a >100x improvement in state estimation accuracy over the SOTA baseline.
• Deployment: The system is now deployed on 100% of the company’s devices and is a foundational component of the platform’s autonomous capabilities.
• Communication: Findings, progress, and results were communicated directly to the immediate team, the entire company, and the CEO.

Current & Future Work

I am currently leading the effort to extend this system for state-anchoring from environmental features. The system will identify and create a persistent library of environmental features, enabling the platform to re-identify them. This capability will provide a consistent global reference frame for the robot’s state, further enhancing precision and long-term reliability.