Don’t start with the robot. Start with the pilot evidence.
A practical 90-day framework for moving from robotics interest to a measurable, controlled Physical AI pilot — with a defined problem, metrics, platform, operating boundary and scale-or-stop decision.
What problem, environment and measurable result are we testing?
Canada's 2026 national AI strategy aims to increase business AI adoption from approximately 12% today to 60% by 2034, and names Manufacturing and Robotics as one of five priority sectors. The practical question is how organizations move from strategy to controlled deployment.
Physical AI changes the risk because the output is a physical action.
Software AI can return a forecast, classification or recommendation. Physical AI connects perception and decision-making to a machine that moves through — or interacts with — the real world.
The result stays primarily digital.
Useful output can still be wrong, but it does not directly move a machine through the environment.
The result becomes a physical action.
The system perceives conditions, decides what to do and sends commands into the real world.
Start where the work is bounded, repetitive and measurable.
Canada's strategy highlights intelligent logistics, autonomous systems and predictive infrastructure maintenance. For mobile robots, four practical pilot domains stand out.
Start with repetitive or difficult-to-staff tasks.
- Visual inspection
- Material-handling research
- Automation experiments
- Human–robot interaction
Evaluate remote information collection while reducing human exposure.
- Industrial inspection
- Mine and facility research
- Hazardous environments
- Sensor integration
Begin with inspection and applied autonomy before broad deployment.
- Autonomous-systems research
- Warehouse studies
- Infrastructure inspection
- Logistics experiments
Connect algorithms with real motion, sensing and environments.
- Embodied AI
- Manipulation
- Reinforcement learning
- Human–robot interaction
These are pilot categories, not claims that any platform is automatically safe, certified or suitable for a specific workplace. Site conditions and applicable requirements still need review.
Humanoid or quadruped? Let the mission pick the body.
The first buying question should not be “Which robot looks most advanced?” It should be “What mission, environment and success metric are we testing?”
Unitree G1 EDU U2
Development-oriented humanoid direction for locomotion, sensing, manipulation and embodied-AI research.
View G1 EDU U2 at SpeedyDrone
Unitree B2
Heavy-duty quadruped direction when terrain, inspection routes, payload carriage and human-exposure reduction matter more than a human form.
View Unitree B2 at SpeedyDroneDevelopment access and humanoid form fit research workflows.
Compact humanoid direction for evaluation and interaction studies.
Configuration-dependent development and manipulation options.
Quadruped mobility for inspection-oriented evaluation. Exact configuration should be assessed project by project.
Industrial quadruped platform for more demanding terrain and payload requirements.
Remote mobile sensing where reducing human exposure is central.
Make the pilot produce evidence for a scale, revise or stop decision.
Ninety days is long enough to expose integration and operating problems, but short enough to preserve a clear decision point.
Define the problem
Start with the current workflow — not the desired robot.
Document task, people, route, frequency, constraints and present cost or risk. Replace “We want a humanoid” with a testable problem statement.
Define success
Choose metrics before the first demonstration.
- Inspection: completion, exposure reduction, data quality, intervention frequency, uptime.
- Research: SDK access, repeatability, simulation workflow, completed experiments.
Select the platform
Match mobility, sensing and development access to the mission.
Run a controlled pilot
Fix the variables and test repeatedly.
- Route and operating zone
- Environmental conditions
- Authorized operators and observers
- Network architecture and data handling
- Payload or sensor configuration
- Test cases, stop conditions and incident logging
A controlled pilot is not a production rollout. Its purpose is to learn where the robot performs reliably and where human intervention remains necessary.
Scale or stop
Make the decision the evidence supports.
Compare results with the Phase 2 metrics and record operating gaps, integration work, training needs and total deployment effort — not only successful runs.
Expand route, duration, payload or operators through another controlled stage.
Identify whether the problem is use case, platform, environment, integration or success threshold.
A robotics pilot crosses business, technical, safety and data ownership.
A useful pilot usually needs more than a robotics champion and a purchase order. Assign decision owners before testing begins.
Business owner
Owns the problem, budget and scale/stop decision.
Technical lead
Owns integration, configuration and experiment design.
Operators
Run the workflow and report real operating friction.
Safety
Defines operating boundaries, procedures and stop conditions.
IT / cybersecurity
Reviews connectivity, access, updates and data flows.
Privacy / procurement
Reviews data, supplier and contract requirements when applicable.
The exact team will vary. A university locomotion project may emphasize researchers, students and laboratory safety. An industrial inspection pilot may require operations, maintenance, safety, IT/OT security and site management.
Robotics pilot FAQ
What is physical AI?
Physical AI is an industry term for systems that perceive, decide and act through a physical machine such as a robot. It is not a single Canadian legal classification or certification.
Does Canada's AI strategy provide automatic funding for a Unitree pilot?
No. The strategy identifies priorities and adoption goals, but it does not automatically fund, approve or endorse a specific Unitree model or customer project.
Why use a 90-day pilot?
Ninety days creates a defined learning period with enough time for repeated tests, operator feedback and integration issues while preserving a firm scale, revise or stop decision.
Should we choose the robot before designing the pilot?
No. Define the problem, environment and success metrics first. Platform selection should follow the mission rather than drive it.
When is a humanoid a reasonable starting point?
A humanoid may be appropriate for embodied AI, locomotion, manipulation or human–robot interaction research where the human-like form is relevant to the experiment.
When is a quadruped a reasonable starting point?
A quadruped may fit mobile inspection, sensing, reconnaissance or terrain-focused research where stable mobility through an operational environment is central.
What should an inspection pilot measure?
Useful measures include mission completion, intervention frequency, data quality, uptime, repeatability and whether the pilot reduces human exposure to the target task or environment.
What happens if the pilot fails?
Document why. The use case, platform, environment, integration, training or metric may be wrong. Stopping or redesigning a poor fit is a valid and often valuable result.
Policy and platform references
- Government of Canada — Canada's National Artificial Intelligence Strategy: AI for All
- Government of Canada — Overview of AI for All
- SpeedyDrone — Canada's 2026 National AI Strategy Puts Robotics in Focus
- SpeedyDrone — Unitree G1 Basic
- SpeedyDrone — Unitree G1 EDU U2
- SpeedyDrone — Unitree B2
Information checked August 12, 2026. This article provides general planning information and does not constitute engineering, workplace safety, cybersecurity, privacy, legal or procurement advice. Platform suitability, requirements and configuration must be assessed for the exact site and project.
Build the pilot around evidence — not around the purchase.
Talk to SpeedyDrone about the problem statement, operating environment, development needs and candidate Unitree platform. Start with the mission — not the machine.
