Add Robotics tag and metadata (#1)

- Add Robotics tag and metadata (9f32e7c6db6bfae848612df947491b188c2dff8a)


Co-authored-by: Vaibhav Srivastav <[email protected]>

README.md

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+---
+library_name: lerobot
+license: apache-2.0
+pipeline_tag: robotics
+tags:
+- robotics
+---
+
+# Model Card for mobile_so100_test_100000
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
+See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
+
+---
+
+## How to Get Started with the Model
+
+For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
+Below is the short version on how to train and run inference/eval:
+
+### 1 Train from scratch
+
+```bash
+python lerobot/scripts/train.py       --dataset.repo_id=${HF_USER}/<dataset>       --policy.type=act       --output_dir=outputs/train/<desired_policy_repo_id>       --job_name=lerobot_training       --policy.device=cuda       --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
+  --wandb.enable=true
+```
+
+*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
+
+### 2 Evaluate the policy
+
+```bash
+python -m lerobot.record       --robot.type=so100_follower       --dataset.repo_id=<hf_user>/eval_<dataset>       --policy.path=<hf_user>/<desired_policy_repo_id>       --episodes=10
+```
+
+Prefix the dataset repo with **eval_** and supply `--policy.path` pointing to a local or hub checkpoint.
+
+---