Robot Chasing IOAI 2026 (Unofficial Mirror)
Predict the next actions of six instruction-following robots in grid worlds. This is an unofficial practice mirror of the IOAI 2026 Robot Chasing task, adapted to standard Kaggle CSV submissions.
Task Overview
Learn the distinct navigation and object-manipulation policies of six robots from labelled grid-world observations, then predict their next actions on unseen situations. There are six robots, each operating in a small room represented by a 6×6 playable area surrounded by walls (full image size 8×8). Each robot receives an English instruction describing a task, and snapshots may be taken at any point while the robot is carrying it out. Your goal is to predict the robot's next action based on these inputs.
Robots do not always follow the shortest path; Robot 0 may behave differently from Robot 1, but each follows its own consistent pattern. Use the training examples to learn these patterns. There are three types of missions: * go to an object (e.g., "approach the red ball") * pick up an object (e.g., "grab the blue key") * put one object next to another (e.g., "place the red box beside the green ball")
The test set may contain new combinations of familiar phrases, colours, and object types, but every element used in the test set appears in the training set.
Data Format
Each sample includes:
* robot_id: Which of the 6 robots (- 0–5).
* image: The room, an 8×8×2 integer array where channel 0 holds categorical object_idx and channel 1 holds categorical colour_idx.
* direction: The direction the robot currently faces.
* mission: The visible natural-language instruction.
* carrying: null or [object_idx, colour_idx] for the carried object.
Rows are independent snapshots in random order; they do not form episodes, and no previous observation or action is available at evaluation time.
Actions
The model must output an integer action code (0–5):
* 0: move up
* 1: move down
* 2: move left
* 3: move right
* 4: pick up
* 5: drop
Movement actions first turn the robot to the absolute direction and then attempt to move it by one cell. A wall or object may block the move, but the direction still changes. Pick up and drop act exclusively on the adjacent target cell defined by the current direction.
Scoring
The scoring is mean per-robot accuracy on a 0–100 scale. Accuracy is first computed independently for each robot, then averaged over all six robots. Every robot therefore has equal weight.