Add robotics task category and improve dataset card

README.md

@@ -1,14 +1,50 @@
 ---
 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>
-    [<a href="https://arxiv.org/abs/2601.10744">arXiv</a>]
 </h2>
 
-## LMEE-Bench
+[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.
+  - `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}
+}
+```