Add dataset card for RoboSemanticBench

README.md

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+---
+task_categories:
+- robotics
+tags:
+- vla
+- semantic-grounding
+- benchmark
+license: other
+---
+
+# RoboSemanticBench
+
+[Paper](https://huggingface.co/papers/2606.02277) | [GitHub](https://github.com/ZGC-EmbodyAI/RoboSemanticBench)
+
+RoboSemanticBench (RSB) is an embodied benchmark for diagnosing **semantic grounding in action prediction** for vision-language-action (VLA) models. In each episode, the robot receives a multiple-choice math or general-knowledge question, observes candidate answer blocks, and must pick the physical block corresponding to the correct answer. This setup isolates the model's semantic decision-making from its low-level manipulation capability.
+
+## Benchmark Suites
+
+The benchmark consists of six suites with 4-choice and 10-choice variants:
+
+| Suite | Task name | Choices | Semantic source |
+| --- | --- | ---: | --- |
+| RSB-Math-4 | `rsb_math` | 4 | Procedural arithmetic |
+| RSB-Math-10 | `rsb_math_10blocks` | 10 | Procedural arithmetic |
+| RSB-HardMath-4 | `rsb_hardmath` | 4 | GSM8K |
+| RSB-HardMath-10 | `rsb_hardmath_10blocks` | 10 | GSM8K |
+| RSB-General-4 | `rsb_general` | 4 | MMLU-style QA |
+| RSB-General-10 | `rsb_general_10blocks` | 10 | MMLU-style QA |
+
+## Metrics
+
+RSB reports three diagnostic metrics:
+
+- 🎯 **Task Success Rate (TSR)**: The episode succeeds only when the robot selects the semantically correct answer block and places it in the answer zone.
+- ✋ **Grasp Success Rate (GSR)**: The robot successfully grasps any candidate answer block, regardless of whether it is correct.
+- 🧭 **Normalized Semantic Grounding (nSG)**: Semantic target selection conditioned on successful grasping, where 0 corresponds to random target selection.
+
+## Sample Usage
+
+### Data Collection
+
+To collect trajectories for a specific task (e.g., RSB-Math-4):
+
+```bash
+# RSB-Math-4
+bash collect_data.sh rsb_math rsb_math_train_500 0 --skip_instructions
+```
+
+### Policy Evaluation
+
+To evaluate a policy using the simulation evaluation entry:
+
+```bash
+python script/eval_policy.py --config policy/Your_Policy/deploy_policy.yml \
+  --overrides \
+  --task_name rsb_general_10blocks \
+  --task_config rsb_general_10blocks_test_500 \
+  --ckpt_setting your_checkpoint_name \
+  --seed 0 \
+  --policy_name Your_Policy
+```
+
+## Citation
+
+```bibtex
+@misc{RoboSemanticBench,
+      title={RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models}, 
+      author={Bin Yu and Yao Zhang and Haishan Liu and Shijie Lian and Yuliang Wei and Xiaopeng Lin and Zhaolong Shen and Changti Wu and Ruina Hu and Bailing Wang and Cong Huang and Kai Chen},
+      year={2026},
+      eprint={2606.02277},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2606.02277}, 
+}
+```