---
license: mit
task_categories:
- robotics
---

<h2 align="center">
  <b>Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration</b>

  <b><i> CVPR 2026</i></b>
</h2>

[Paper](https://arxiv.org/abs/2601.10744) | [Project Page](https://wangsen99.github.io/papers/lmee/) | [GitHub](https://github.com/wangsen99/LMEE)

LMEE-Bench is a benchmark for **Long-term Memory Embodied Exploration (LMEE)**. It is designed to evaluate an agent's exploratory cognition and decision-making behaviors by incorporating multi-goal navigation and memory-based question answering tasks.

## Dataset Structure
The benchmark consists of the following components:
  - `lmee_bench_sub`: Includes 58 tasks.
  - `lmee_bench`: Includes the full 166 tasks.
  - `task_test`: Trajectory data test set.

## Sample Usage (Evaluation)

To evaluate a model on LMEE-Bench, you can follow the instructions provided in the [official repository](https://github.com/wangsen99/LMEE).

### 1. Reasoning
Specify the paths in the configuration file `cfg/eval_lmee_bench.yaml` and execute the following command:

```bash
python run_lmee.py -cf cfg/eval_lmee_bench.yaml --answer_type open
```
- **answer_type**: Choose between `open` and `choice`.

### 2. Evaluation
After running the reasoning script, you will get a results file (e.g., `lmee_answer.json`). Use the following command to evaluate the question-answering performance:

```bash
python eval_lmee_bench.py --json_path "results/exp_eval_lmee/lmee_answer.json" --root_dir "../data/LMEE-Bench/task_test"
```

## Citation
```bibtex
@inproceedings{wang2026explore,
  title={Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration},
  author={Wang, Sen and Liu, Bangwei and Gao, Zhenkun and Ma, Lizhuang and Wang, Xuhong and Xie, Yuan and Tan, Xin},
  booktitle={Proceedings of the IEEE/CVF Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}
```