Add English README with intro, usage, and training data link

README.md

@@ -1,19 +1,95 @@
 ---
-license: bsd-3-clause
+license: apache-2.0
 tags:
-- reinforcement-learning
-- isaac-lab
-- unitree-go2
-- unitree-z1
-- locomotion
+  - reinforcement-learning
+  - robotics
+  - quadruped
+  - locomotion
+  - isaac-lab
+  - ppo
+  - rsl-rl
+library_name: rsl-rl
+pipeline_tag: reinforcement-learning
 ---
 
-# Go2+Z1 Walking Policy (RSL-RL PPO, 1500 iters)
+# Go2+Z1 Walking Policy (V1, state-only PPO)
 
-Trained in NVIDIA Isaac Sim 6.0.0.0 + Isaac Lab develop with PPO on
-`Isaac-Velocity-Flat-Go2Z1-v0`. Robot: Unitree Go2 quadruped with a Z1 6-DOF
-arm folded on its back (startFlat pose).
+PPO walking policy for the **Unitree Go2 + Z1** composite robot (12 leg DOFs + 6 arm DOFs = 18 DOF), trained in Isaac Lab on flat ground while holding the Z1 arm folded on the back.
 
-- 4096 parallel envs
-- 1500 iters, ~33 min wall time on RTX Pro 6000 Blackwell (96 GB)
-- Final mean reward 5.4, success rate ~100% at iter 700+
+## Highlights
+
+- Backbone: rsl-rl `OnPolicyRunner` actor-critic (MLP 512-256-128, ELU)
+- Task: `Isaac-Velocity-Flat-Go2Z1-v0` (forward/lateral linear vel + small yaw rate commands)
+- 4096 parallel envs × 1500 PPO iters on a single RTX PRO 6000 Blackwell (96 GB)
+- Z1 arm forced to remain in the folded "startFlat" pose during locomotion
+- Verified: walks 10 m inside the real `Simple_Warehouse/warehouse.usd` (3/3 episodes)
+
+## Files
+
+- `model_*.pt` — checkpoint dictionaries with `actor_state_dict` / `critic_state_dict`
+
+## Architecture
+
+```
+Actor MLP : Linear(obs→512) ELU Linear(512→256) ELU Linear(256→128) ELU Linear(128→12)
+Critic MLP: same shape, single value head
+Inputs    : base lin_vel + ang_vel + projected_gravity + commands + joint_pos + joint_vel + last_action
+Outputs   : 12 leg joint position deltas (Go2 hip/thigh/calf × 4)
+```
+
+## Usage
+
+```python
+import torch, torch.nn as nn
+
+# Load checkpoint
+state = torch.load("model_1499.pt", map_location="cuda:0", weights_only=False)
+sd = state["actor_state_dict"]
+
+# Rebuild actor (3 hidden layers + output)
+h, obs_dim = sd["mlp.0.weight"].shape[0], sd["mlp.0.weight"].shape[1]
+act_dim = sd["mlp.6.weight"].shape[0]
+actor = nn.Sequential(
+    nn.Linear(obs_dim, h), nn.ELU(),
+    nn.Linear(h, h), nn.ELU(),
+    nn.Linear(h, h), nn.ELU(),
+    nn.Linear(h, act_dim),
+).cuda().eval()
+actor.load_state_dict({k.replace("mlp.", ""): v for k, v in sd.items() if k.startswith("mlp.")})
+
+# obs comes from Isaac Lab's Isaac-Velocity-Flat-Go2Z1-Play-v0 env
+with torch.inference_mode():
+    action = actor(obs)
+```
+
+For end-to-end inference inside Isaac Sim, see [`stage4_joint_eval/walk_in_real_warehouse.py`](https://github.com/aws300/go2_z1_warehouse/blob/main/go2_z1_warehouse/stage4_joint_eval/walk_in_real_warehouse.py).
+
+## Training data
+
+This is an **on-policy RL** model — no offline dataset is used. The policy is trained from scratch by interacting with the simulator. The full task definition (rewards, observations, terminations) lives in:
+
+- Repo: <https://github.com/aws300/go2_z1_warehouse>
+- Task config: `go2_z1_warehouse/stage1_walking/{flat_env_cfg.py, rough_env_cfg.py}`
+
+## Eval results
+
+| Scenario | Episodes | Success | Mean traveled |
+|---|---|---|---|
+| Flat plane | 10 | 100 % | — |
+| 4 cuboid shelves | 5 | 80 %  | 11.21 m |
+| Real `warehouse.usd` | 3 | 100 % | 10.00 m |
+
+## Citation
+
+```bibtex
+@misc{go2z1-walking-v1,
+  title  = {Go2+Z1 Warehouse Walking Policy V1 (state-only PPO)},
+  author = {m3},
+  year   = {2026},
+  url    = {https://huggingface.co/m3/go2z1-walking-rsl-rl-v1}
+}
+```
+
+## Successor
+
+- V2 (rotation-capable + heading-tracking): [m3/go2z1-walking-rsl-rl-v2](https://huggingface.co/m3/go2z1-walking-rsl-rl-v2)