developerabu/qefro-decision-jef-c1
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syed abuthahir
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🍳 The RoboCasa Kitchen Leaderboard
What does it take for a robot to handle kitchen chores the way a person does? It has to see (Vision), understand instructions (Language), and actually act (Action) — and VLA (Vision-Language-Action) models are emerging as the answer. They're the bridge between large multimodal models and real-world embodied control.
RoboCasa Kitchen is a leading robot-learning benchmark in which a single-arm robot (Franka Panda) performs 24 atomic manipulation tasks — picking up cups and bowls, opening drawers and doors, turning faucets, pressing buttons, and more — inside a photorealistic simulated kitchen. Because the layout and object placement are randomized every episode, it tests genuine generalization rather than memorized motions. The score (success rate, SR) is the average fraction of the 24 tasks completed as instructed, measured over multiple seeds so results aren't down to luck.
The catch: this benchmark has no official leaderboard, and protocols (number of demonstrations, evaluation setup) differ from paper to paper, leaving scores scattered. Lining the numbers up naively quickly turns into an apples-to-oranges comparison.
This leaderboard fixes that by collecting published scores with their sources and comparing only what is genuinely comparable. It's split into three tables:
🏆 Kitchen 24-task (matched) — head-to-head under identical conditions (per the RLDX-1 Technical Report). This is the core ranking you can actually trust.
➕ Other protocols — self-reported under different setups (e.g. fewer demos). Not directly comparable, so kept separate.
🤖 GR1-Tabletop — a different, humanoid-based variant suite, separated to avoid confusion.
Any researcher can submit their own model's score directly, and submissions are reviewed before they appear on the board. Every number links to its source paper, so you can verify it yourself.
👉 ginigen-ai/robocasa-kitchen-leaderboard
What does it take for a robot to handle kitchen chores the way a person does? It has to see (Vision), understand instructions (Language), and actually act (Action) — and VLA (Vision-Language-Action) models are emerging as the answer. They're the bridge between large multimodal models and real-world embodied control.
RoboCasa Kitchen is a leading robot-learning benchmark in which a single-arm robot (Franka Panda) performs 24 atomic manipulation tasks — picking up cups and bowls, opening drawers and doors, turning faucets, pressing buttons, and more — inside a photorealistic simulated kitchen. Because the layout and object placement are randomized every episode, it tests genuine generalization rather than memorized motions. The score (success rate, SR) is the average fraction of the 24 tasks completed as instructed, measured over multiple seeds so results aren't down to luck.
The catch: this benchmark has no official leaderboard, and protocols (number of demonstrations, evaluation setup) differ from paper to paper, leaving scores scattered. Lining the numbers up naively quickly turns into an apples-to-oranges comparison.
This leaderboard fixes that by collecting published scores with their sources and comparing only what is genuinely comparable. It's split into three tables:
🏆 Kitchen 24-task (matched) — head-to-head under identical conditions (per the RLDX-1 Technical Report). This is the core ranking you can actually trust.
➕ Other protocols — self-reported under different setups (e.g. fewer demos). Not directly comparable, so kept separate.
🤖 GR1-Tabletop — a different, humanoid-based variant suite, separated to avoid confusion.
Any researcher can submit their own model's score directly, and submissions are reviewed before they appear on the board. Every number links to its source paper, so you can verify it yourself.
👉 ginigen-ai/robocasa-kitchen-leaderboard