# CookBench: A Long-Horizon Embodied Planning Benchmark for Complex Cooking Scenarios

arXiv: https://arxiv.org/abs/2508.03232 · HTML: https://mlsanity.com/paper/2508.03232/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2025.

## Built on by (most cited first, 11 of 11)

- [RoboAgent: Chaining Basic Capabilities for Embodied Task Planning](https://mlsanity.com/paper/2604.07774/) (2026)
- [DeliveryBench: Can Agents Earn Profit in Real World?](https://mlsanity.com/paper/2512.19234/) (2025)
- [Video2Layout: Recall and Reconstruct Metric-Grounded Cognitive Map for Spatial Reasoning](https://mlsanity.com/paper/2511.16160/) (2025)
- [TongSIM: A General Platform for Simulating Intelligent Machines](https://mlsanity.com/paper/2512.20206/) (2025)
- [World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning](https://mlsanity.com/paper/2604.26934/) (2026)
- [ParaCook: On Time-Efficient Planning for Multi-Agent Systems](https://mlsanity.com/paper/2510.11608/) (2025)
- [Scaling Spatial Reasoning in MLLMs through Programmatic Data Synthesis](https://mlsanity.com/paper/2512.16237/) (2025)
- [Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning](https://mlsanity.com/paper/2608.07558/) (2026)
- [UniETP: Unifying Environments for Generalizable Embodied Task Planning](https://mlsanity.com/paper/2607.18062/) (2026)
- [GraphThink: Graph-Enhanced LLM Thinking for Long-Horizon Embodied Task Planning](https://mlsanity.com/paper/2608.07905/) (2026)
- [NutriBench-Kitchen: Benchmarking Embodied AI for Nutrition Management](https://mlsanity.com/paper/2609.07135/) (2026)

## Abstract (arXiv)

Embodied Planning is dedicated to the goal of creating agents capable of executing long-horizon tasks in complex physical worlds. However, existing embodied planning benchmarks frequently feature short-horizon tasks and coarse-grained action primitives. To address this challenge, we introduce CookBench, a benchmark for long-horizon planning in complex cooking scenarios. By leveraging a high-fidelity simulation environment built upon the powerful Unity game engine, we define frontier AI challenges in a complex, realistic environment. The core task in CookBench is designed as a two-stage process. First, in Intention Recognition, an agent needs to accurately parse a user's complex intent. Second, in Embodied Interaction, the agent should execute the identified cooking goal through a long-horizon, fine-grained sequence of physical actions. Unlike existing embodied planning benchmarks, we refine the action granularity to a spatial level that considers crucial operational information while abstracting away low-level robotic control. Besides, We provide a comprehensive toolset that encapsulates the simulator. Its unified API supports both macro-level operations, such as placing orders and purchasing ingredients, and a rich set of fine-grained embodied actions for physical interaction, enabling researchers to focus on high-level planning and decision-making. Furthermore, we present an in-depth analysis of state-of-the-art, closed-source Large Language Model and Vision-Language Model, revealing their major shortcomings and challenges posed by complex, long-horizon tasks. The full benchmark will be open-sourced to facilitate future research.