Robita/Flexiv Robotic Surface Finishing
Shows a Robita/Flexiv surface-finishing workflow from trajectory setup in software to execution on a workpiece with sanding and polishing passes.
The video emphasizes teachable robot paths, adaptive force control, and process repeatability for practical finishing deployments.
Flexiv
Adaptive Robotics
Use case
polishing
Category
Surface Finishing And Material Removal
Key capability
force control
Storyboard
What the video shows
Automatically perform surface finishing by learning trajectories and controlling force applied by the robot.
-
Step 1
Connect and initialize the Robita/Flexiv robot via software interface.
-
Step 2
Set trajectory learning parameters including force and moment control for adaptive finishing.
-
Step 3
Program and save multiple waypoints defining the robot's path over the surface.
-
Step 4
Execute the learned trajectory with real-time force control for precise material removal.
-
Step 5
Monitor and adjust parameters as needed to optimize finishing performance.
Challenge
Why this task is difficult
Manual surface finishing is inconsistent and labor-intensive, requiring precision and adaptive force control.
Value
Operational value
Improves surface finish quality, reduces operator workload, and ensures consistent, repeatable processing.
Deployment layer
How Robita AI helps
Robita AI turns this kind of Flexiv demonstration into a deployment plan: we assess the manual workflow, define the tooling and fixture assumptions, validate the robot capability, and map the pilot path from first test to production rollout. For surface finishing and material removal applications, that means connecting the visible robot motion to practical questions like cycle time, safety, operator handoff, data capture, and integration with the existing workstation.
Complexity reduction
How Flexiv force control reduces complexity
Flexiv force control lets the robot adapt during contact instead of relying only on exact position commands. That reduces the need for heavy custom mechanics, perfectly rigid fixtures, and long exception programming because the robot can feel insertion, pressure, and surface contact while it works.