113 lines
2.7 KiB
Markdown
113 lines
2.7 KiB
Markdown
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# StackForce SimReady Isaac Lab 导出工程
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这个工程由 StackForce SimReady 导出，可直接用于 Isaac Lab / Isaac Sim 训练。
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### 直接可复制的训练命令
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```bash
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conda activate <你自己的IsaacLab环境名称>
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cd <exported_project>
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python -m pip install -e source/stackforce_simready_2foot_lab
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python scripts/list_envs.py
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python scripts/zero_agent.py --task Template-2foot-Direct-v0 --headless --num_envs 8
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python scripts/rsl_rl/train.py --task Template-2foot-Direct-v0 --headless --num_envs 64 --max_iterations 100
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```
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如果你想打开 Isaac Sim 窗口，把训练命令里的 `--headless` 去掉即可。
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### 推荐 Isaac Lab / Isaac Sim 环境
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本导出工程推荐使用下面这套已验证配置：
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```text
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Python 3.11
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Isaac Sim 5.1.0
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Isaac Lab v2.3.2 / pip 2.3.2.post1
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Torch 2.7.0+cu128
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Torchvision 0.22.0+cu128
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LeggedGym-Ex 0.3.0 提供的 rsl_rl
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```
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导出包内已包含一键环境脚本：
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```bash
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chmod +x scripts/setup_stackforce_isaac_lab_sim_env.sh
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./scripts/setup_stackforce_isaac_lab_sim_env.sh
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```
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脚本默认创建 `env_isaaclab`。如果你想改环境名：
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```bash
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ENV_NAME=my_isaaclab ./scripts/setup_stackforce_isaac_lab_sim_env.sh
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```
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### 训练输出和 checkpoint
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训练输出在：
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```text
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logs/rsl_rl/<experiment_name>/<timestamp>/
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```
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可用下面的命令直接查找：
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```bash
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find logs -name "*.pt"
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find logs -name "*.pt" | sort | tail -n 1
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find logs -name "policy.onnx"
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```
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Training saves `model_final.pt` and also attempts to export `exported/policies/policy.onnx`.
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### 继续训练
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```bash
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python scripts/rsl_rl/train.py --task Template-2foot-Direct-v0 --resume --load_run <run_dir_name> --checkpoint <model_x.pt>
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```
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### 播放训练后的策略
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```bash
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python scripts/rsl_rl/play.py --task Template-2foot-Direct-v0 --num_envs 1 --disable_resets
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```
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如需加载指定 checkpoint：
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```bash
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python scripts/rsl_rl/play.py --task Template-2foot-Direct-v0 --checkpoint logs/rsl_rl/<experiment_name>/<timestamp>/model_final.pt --num_envs 1 --disable_resets
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```
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Regenerate ONNX from a checkpoint:
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```bash
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python scripts/rsl_rl/play.py --task Template-2foot-Direct-v0 --checkpoint logs/rsl_rl/<experiment_name>/<timestamp>/model_final.pt --num_envs 1 --disable_resets --export_onnx --num_steps 1
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```
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### 增加自定义 Reward
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编辑：
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```text
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source/stackforce_simready_2foot_lab/stackforce_simready_2foot_lab/tasks/direct/2foot/custom_rewards.py
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```
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在 `compute_custom_reward_terms(env)` 中返回你自己的 reward term。
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然后再编辑：
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```text
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source/stackforce_simready_2foot_lab/stackforce_simready_2foot_lab/tasks/direct/2foot/2foot_env_cfg.py
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```
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把：
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```python
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"custom_reward": 0.0
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```
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改成非零，比如：
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```python
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"custom_reward": 1.0
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```
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