Vision Language Action Models in Production

Wrote delivery plan from research prototypes

(Note: To respect previous employer privacy, this discussion focuses on high-level architecture, scoping process, and industry-shared practices.)

Situation

The team was challenged to enable a robotics platform to execute complex, real-world tasks based on natural language voice commands combined with visual inputs from the robot’s environment. This required integrating cutting-edge Vision-Language-Action (VLA) models into a real-time production system.

Task

The primary organizational challenge was a lack of existing deep neural network (DNN) and ML deployment capability within our team of dedicated C++ robotics developers. This created a significant bottleneck in defining the feasibility and intermediate steps for implementing a VLA system.

My main tasks were to bridge this gap and define the full end-to-end strategy, including:

  • Providing high-level guidance on VLA model architectures and deployment methods.
  • Translating the vague product vision into a concrete, executable system diagram and delivery plan.
  • Scoping personal technical contributions in key robotics areas essential for VLA execution.

Action

Leveraging my prior ML and data experience, I led the technical scoping process, moving the project from vague vision to a detailed, actionable plan broken down into Epics and granular tickets.

A. System and Deployment Architecture

I designed the high-level system flow:

  • Input Pipeline: Defined the flow from voice command (Speech-to-Text translation) to the central LLM/VLA model, including the fusion of text and visual data (images from the robot).
  • Local Deployment Scoping: Investigated and scoped optimal inference options for local, on-robot deployment, including selecting appropriate tooling and defining alignment matrices for input/output sizes between different model components (e.g., visual encoders, LLM output, motion planners).
  • Deployment Procedures: Established the necessary steps for model finetuning and updating in a production environment.

B. Personal Technical Contributions (Interface Design & Bug Resolution)

My personal contributions were critical in enabling the successful execution of the model’s output:

  • Path Planning: Designed robust interfaces for the LLM output to seamlessly integrate with our existing path planning modules, and resolved critical bugs in the system’s ability to translate high-level action commands into executable trajectories.
  • Motion Control: Designed and refined the interfaces and logic within the motion control modules to ensure the robot could reliably execute the LLM-derived movement commands, resolving bugs related to physical constraints and dynamic control.

Result

While ultimately the project implementation was transferred to another team due to an internal restructuring and insufficient local manpower, the action phase yielded a crucial, tangible result:

  • Established a Concrete Blueprint: I successfully created a high-level system diagram, Epics, and a detailed ticket breakdown, providing a production-ready roadmap for VLA implementation. This work validated the feasibility of the concept and reduced the unknown technical risk for the organization.
  • Demonstrated Technical Leadership: My ability to scope the project and define technical requirements acted as a crucial supplement to the team’s C++ expertise, demonstrating the ability to rapidly integrate and plan advanced ML technologies.

This experience represents a significant “stretch project” in my portfolio, highlighting my deep interest and competency in physical AI and my belief in the technology’s transformative potential for streamlining and simplifying future robotics development.