VLA (Vision-Language-Action)
Model connecting perception to action
A core model that uses visual input and task context to determine what action should be taken on site.
VLA-Powered Physical AISolutions
Mobile manipulators with edge AI automate flexible tasks requiring movement. Cut staffing requirements in half with next-generation robotics.
We do not stop at one-off prototypes. We build systems that can keep learning on site and improve automation performance after deployment.
Three Building Blocksof the Physical AI System
The system combines an intelligence layer, real hardware, and an operations foundation built for practical field use.
Mobile Manipulator
Mobile Work Robot
A real hardware system combining an autonomous mobile base, robot arm, and cameras for movement and light work.
Remote Operations and Data Layer
Foundation for monitoring and improvement
Centralizes robot status, logs, remote intervention, and learning data so operational improvement is easier to sustain.
Manual dependencies still createcritical factory bottlenecks
Many recurring site tasks look simple, but they are hard to staff, difficult to standardize, and expensive to keep running reliably.
Hiring difficulty
Night shifts and repetitive work are especially hard to staff, making production planning fragile.
Operational dependency
Patrol, verification, transport, and setup support often depend on tacit knowledge held by specific operators.
Cost of maintaining uptime
Teams absorb continuous movement, checking, and coordination work just to keep the line running.
Where deployment canstart most effectively
Early deployments work best in tasks that happen frequently and create repeated movement or monitoring load on site.
Line patrol and meter checks
Collect equipment state, meter values, and signs of delay through recurring patrol operations.
- Patrol
- Meter Checks
- Anomaly Monitoring
Transport, supply, and collection
Automate small-lot transport, line-side supply, and collection work between process steps.
- Transport
- Supply
- Collection
Packing-adjacent support tasks
Start from support work such as insertion help and simple setup tasks before expanding the scope.
- Support Work
- Light Tasks
- Setup
A deployment flow that goesbeyond PoC
We combine standard deployment, remote operation, data accumulation, learning, evaluation, and redeployment into one delivery model.
Scope the site
Clarify the target workflow, constraints, safety assumptions, and the right evaluation metrics.
Launch with a standard setup
Bring in a standard package of robot hardware, remote operation, and logging to establish a working first deployment.
Accumulate data and train
Use remote operation and live logs to improve the mapping between perception and action while reducing intervention.
Evaluate and redeploy
Only validated improvements are rolled out, enabling safe and repeatable iteration in production settings.
Proven Experiencein Real Environments
ManmaruAI is grounded in software engineering and is now focused on bringing robotics and AI into practical field operations.
AMD Robotics Hackathon のデモ動画
Software delivery background
We have supported multiple client projects across scoping, implementation, and iterative improvement.
Deep-tech experience
Our experience includes technically demanding domains such as quantum computing and satellite-related development.
Hardware-backed validation
We develop and validate VLA control systems through practical testing in demo and near-production environments.
Client work
Science Aid株式会社
BlendAI株式会社
- ASMA Inc.
ASMA株式会社
Tech Blog
We share implementation notes, experiments, and technical learnings around robotics and Physical AI on note.
Start by scoping the rightautomation target
We can help define where to start, what level of automation is realistic, and how to build a rollout path that fits your operational constraints.