# Supplementary Videos

A shortcut learning framework for improving Task and Motion Planning (TAMP) through learned dynamic shortcuts using model-free Reinforcement Learning (RL). This folder contains supplementary videos along with our ICLR 2026 submission.

## Contents

- `video.mp4`: High-level overview of our motivation, approach, and main results.
- `obstacle_2D_slap.mp4`: Evaluation results of our approach Shortcut Learning for Abstract Planning (SLAP) on Obstacle 2D environment.
- `obstacle_2D_pure_planning.mp4`: Evaluation results of Pure Planning baseline on Obstacle 2D environment.
- `obstacle_tower_slap.mp4`: Evaluation results of our approach SLAP on Obstacle Tower (PyBullet) environment.
- `obstacle_tower_pure_planning.mp4`: Evaluation results of Pure Planning baseline on Obstacle Tower (PyBullet) environment.
- `cluttered_drawer_slap.mp4`: Evaluation results of our approach SLAP on Cluttered Drawer (PyBullet) environment.
- `cluttered_drawer_pure_planning.mp4`: Evaluation results of Pure Planning baseline on Cluttered Drawer (PyBullet) environment.
- `cleanup_table_slap.mp4`: Evaluation results of our approach SLAP on Cleanup Table (PyBullet) environment.
- `cleanup_table_pure_planning.mp4`: Evaluation results of Pure Planning baseline on Cleanup Table (PyBullet) environment.
