AdaReP:Adaptive Re-Planning under Model Mismatch for Neural World-Model Predictive Control
- Published
- Source
- arXiv
- Paper number
- 470
- Field
- Robotics
- arXiv ID
- 2606.23079
Key points
- It defines an adaptive replanning threshold based on dynamic regret theory, computed from prediction deviation and local dynamics sensitivity.
- It requires no training and can be applied simply by wrapping existing world models and planners without modifying them.
- On VP2 and RoboDesk, it reduces NFE by 59 percent, and on the DMC Suite with TD-MPC2, it reduces NFE by 54.5 percent.
- On a real Franka robot, it cuts queries by more than 80 percent over 50 trials while keeping the success count at 36 out of 50, compared with 34 out of 50 for the baseline.
- In sensitive hinge-like regions, replanning frequency increases automatically, while in smooth regions the system reuses cached plans for longer.
Paper links
External research summaries. These are not HDATF publications or measured product results.