Robotics planningAdvanced
Learned Feasibility Prediction for Robot Plans
A feasibility predictor estimates which symbolic actions are likely to survive expensive motion checks.
Site connection
The TAMP project trains feasibility guidance from successful and failed traces while keeping motion checking as the verifier.
Visual model
Guidance before verification
The predictor ranks or prunes plans before IK, collision, and trajectory checks spend real compute.
Interactive
Learning can rank plans, but geometry still verifies them
1Symbolic skeletonplanner space
2Arm assignmentplanner space
3IK checkmotion verifier
4Collision checkmotion verifier
5Trajectorymotion verifier
6Trace logtraining data
The Supervision Signal
Every failed IK call and collision check is useful data. A trace tells the learner what kind of partial plan was plausible symbolically but bad geometrically.
What It Should Not Do
The predictor should not declare a plan safe. It should only help decide what to try first or what to avoid trying too often.
Common Pitfalls
- Training only on successful traces.
- Letting the predictor replace collision checking.
- Optimizing median planning time while ignoring rare catastrophic failures.
Quick check
Quiz
What is the final verifier in this architecture?
- The learned predictor
- IK, collision, trajectory, and execution checks
- A CSS animation
- The task name
Learned guidance helps search, but geometric and execution checks verify feasibility.