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

README.md

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+---
+license: apache-2.0
+tags:
+  - reinforcement-learning
+  - robotics
+  - quadruped
+  - manipulation
+  - recovery
+  - isaac-lab
+  - ppo
+  - rsl-rl
+library_name: rsl-rl
+pipeline_tag: reinforcement-learning
+---
+
+# Go2+Z1 Standup Recovery Policy (RL, full 18-DOF)
+
+PPO policy that lets the **Unitree Go2 + Z1** composite robot recover from arbitrary fallen poses by using its **legs and Z1 arm together as a self-righting kinematic chain**, then automatically folds the Z1 back to the carry pose once the trunk is upright.
+
+## Behaviour
+
+1. Reset spawns the robot at `trunk_z = 0.18 m` with random orientation (full quaternion sampled by `reset_root_state_with_random_orientation`)
+2. Policy commands all 18 joints (12 leg + 6 arm) for up to 5 s
+3. Reward favours: trunk lifted, projected gravity aligned with -Z, Z1 close to its `Z1_FOLDED_DEFAULT` pose, sparse +10 success bonus when all three are satisfied
+4. Episode ends if `trunk_z < 0.05 m` (collapsed) or time-out
+
+## Highlights
+
+- 4096 parallel envs × 3000 PPO iters
+- Mean reward climbs from 0.6 (random) → 89+ (final)
+- `standup_success` sparse bonus reaches 8.6 / episode (≈86 % of timesteps satisfy the success criterion)
+- `trunk_collapsed` termination rate ≈ 0 — robot does not give up
+
+## Architecture
+
+Same rsl-rl actor-critic shape as our walking policies (3-layer MLP 512-256-128 ELU). Action dim = 18.
+
+## Reward composition
+
+```python
+trunk_height_reward      weight  +5.0   # clamp(z / 0.32, 0, 1)
+upright_alignment        weight  +3.0   # clamp(-projected_gravity_b[2], 0, 1)
+z1_fold                  weight  +2.0   # exp(-||z1_pos - Z1_FOLDED_DEFAULT||)
+standup_success (sparse) weight +10.0   # 1 if z>0.28 ∧ upright>0.92 ∧ fold_err<0.3
+action_rate_l2           weight -0.005
+joint_acc_l2             weight -2.5e-7
+joint_torques_l2         weight -1e-5
+```
+
+## Files
+
+- `standup_v1.pt` — rsl-rl `OnPolicyRunner` checkpoint
+
+## Usage
+
+```python
+import torch, torch.nn as nn
+
+state = torch.load("standup_v1.pt", map_location="cuda:0", weights_only=False)
+sd = state["actor_state_dict"]
+h, obs_dim = sd["mlp.0.weight"].shape[0], sd["mlp.0.weight"].shape[1]
+act_dim = sd["mlp.6.weight"].shape[0]   # 18 for full-body recovery
+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.")})
+```
+
+For a full integration example (drop fallen robot into warehouse, run standup, verify Z1 auto-folds), see [`stage4_joint_eval/standup_recovery.py`](https://github.com/aws300/go2_z1_warehouse/blob/main/go2_z1_warehouse/stage4_joint_eval/standup_recovery.py).
+
+## Training data
+
+On-policy RL — no offline dataset. The Isaac Lab task is registered as `Isaac-Standup-Go2Z1-v0` and lives at:
+
+- Repo: <https://github.com/aws300/go2_z1_warehouse>
+- Task config: `go2_z1_warehouse/stage5_standup/standup_env_cfg.py`
+- Training launcher: `go2_z1_warehouse/stage5_standup/train_launcher.py`
+
+## Citation
+
+```bibtex
+@misc{go2z1-standup-v1,
+  title  = {Go2+Z1 Standup Recovery Policy (RL, full 18-DOF)},
+  author = {m3},
+  year   = {2026},
+  url    = {https://huggingface.co/m3/go2z1-standup-rl-v1}
+}
+```