| --- |
| 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} |
| } |
| ``` |
