add some codes to make retain come true

rl_game/demo/__init__.py

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+import gymnasium as gym
+
+# 导入你的配置
+from rl_game.demo.config.t1_env_cfg import T1EnvCfg
+
+# 注册环境到 Gymnasium
+gym.register(
+    id="Isaac-T1-Walking-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv", # Isaac Lab 统一的强化学习环境入口
+    kwargs={
+        "cfg": T1EnvCfg(),
+    },
+)

rl_game/demo/config/ppo_cfg.yaml

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+params:
+  seed: 42
+  algo:
+    name: a2c_continuous
+
+  model:
+    name: continuous_a2c_logstd
+
+  network:
+    name: actor_critic
+    separate: False
+    space:
+      continuous:
+        mu_activation: None
+        sigma_activation: None
+        mu_init:
+          name: default
+        sigma_init:
+          name: const_initializer
+          val: 0
+        fixed_sigma: True
+    mlp:
+      units: [512, 256, 128]
+      activation: elu
+      d2rl: False
+      initializer:
+        name: default
+
+  config:
+    name: T1_Walking
+    env_name: rlgym # Isaac Lab 包装器
+    multi_gpu: False
+    ppo: True
+    mixed_precision: True
+    normalize_input: True
+    normalize_value: True
+    value_bootstrap: True
+    num_actors: 16384 # 同时训练的机器人数量
+    reward_shaper:
+      scale_value: 1.0
+    normalize_advantage: True
+    gamma: 0.99
+    tau: 0.95
+    learning_rate: 3e-4
+    lr_schedule: adaptive
+    kl_threshold: 0.008
+    score_to_win: 20000
+    max_epochs: 5000
+    save_best_after: 50
+    save_frequency: 100
+    grad_norm: 1.0
+    entropy_coef: 0.01
+    truncate_grads: True
+    bounds_loss_coef: 0.0
+    e_clip: 0.2
+    horizon_length: 128
+    minibatch_size: 32768
+    mini_epochs: 5
+    critic_coef: 2
+    clip_value: True

rl_game/demo/config/t1_env_cfg.py

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+from isaaclab.envs import ManagerBasedRLEnvCfg
+from isaaclab.managers import ObservationGroupCfg as ObsGroup
+from isaaclab.managers import ObservationTermCfg as ObsTerm
+from isaaclab.managers import RewardTermCfg as RewTerm
+from isaaclab.managers import TerminationTermCfg as DoneTerm
+from isaaclab.envs.mdp import JointPositionActionCfg
+import isaaclab.envs.mdp as mdp
+from isaaclab.utils import configclass
+from rl_game.demo.env.t1_env import T1SceneCfg
+
+@configclass
+class T1ObservationCfg:
+    """观察值空间配置容器"""
+
+    @configclass
+    class PolicyCfg(ObsGroup):
+        concatenate_terms = True
+        enable_corruption = False
+
+        # ⬅️ 2. 修改点：直接使用 mdp.函数名，不要引号
+        base_lin_vel = ObsTerm(func=mdp.base_lin_vel)
+        base_ang_vel = ObsTerm(func=mdp.base_ang_vel)
+        projected_gravity = ObsTerm(func=mdp.projected_gravity)
+        joint_pos = ObsTerm(func=mdp.joint_pos_rel)
+        joint_vel = ObsTerm(func=mdp.joint_vel_rel)
+        actions = ObsTerm(func=mdp.last_action)
+
+    policy = PolicyCfg()
+
+@configclass
+class T1ActionCfg:
+    """动作空间配置"""
+    joint_pos = JointPositionActionCfg(
+        asset_name="robot", # 注意这里是 asset_name，对应场景里的机器人名称
+        joint_names=[".*"], # 控制所有关节
+        scale=0.5, # 缩放网络输出
+        use_default_offset=True # 动作是相对于默认关节角度(init_state里的0)的偏移
+    )
+
+@configclass
+class T1TerminationsCfg:
+    """终止条件：什么时候重置环境"""
+    # 1. 摔倒重置：如果躯干高度低于 0.35米 (假设 T1 胯部在 0.7米)
+    base_height_too_low = DoneTerm(
+        func=mdp.root_height_below_minimum,
+        params={"minimum_height": 0.35},
+    )
+    # 2. 存活时间限制 (Timeout)
+    time_out = DoneTerm(func=mdp.time_out)
+
+@configclass
+class T1CommandsCfg:
+    """命令配置：定义机器人的目标速度"""
+    base_velocity = mdp.UniformVelocityCommandCfg(
+        asset_name="robot",
+        resampling_time_range=(10.0, 10.0),
+        ranges=mdp.UniformVelocityCommandCfg.Ranges(
+            lin_vel_x=(0.5, 1.5),
+            lin_vel_y=(0.0, 0.0),
+            ang_vel_z=(-0.1, 0.1),
+        ),
+    )
+
+@configclass
+class T1RewardCfg:
+    """奖励函数配置：鼓励向前走，惩罚摔倒和过大能耗"""
+    # 速度追踪奖励 (假设目标是沿 X 轴走)
+    track_lin_vel_xy_exp = RewTerm(
+        func=mdp.track_lin_vel_xy_exp,
+        weight=1.0,
+        params={
+            "std": 0.5,
+            "command_name": "base_velocity"
+        }
+    )
+    # 姿态惩罚 (保持上半身直立)
+    upright = RewTerm(func=mdp.flat_orientation_l2, weight=0.1)
+    # 动作平滑惩罚
+    action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-0.01)
+
+@configclass
+class T1EnvCfg(ManagerBasedRLEnvCfg):
+    """主环境配置"""
+    # 场景设置
+    scene = T1SceneCfg(num_envs=16384, env_spacing=2.5)
+    # 观察与奖励
+    observations = T1ObservationCfg()
+    rewards = T1RewardCfg()
+    terminations = T1TerminationsCfg()
+    actions = T1ActionCfg()
+    commands = T1CommandsCfg()
+    episode_length_s = 20.0
+    # 默认步长
+    decimation = 6 # 仿真频率/控制频率

rl_game/demo/env/t1_env.py

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+from isaaclab.assets import ArticulationCfg, AssetBaseCfg
+from isaaclab.scene import InteractiveSceneCfg
+from isaaclab.utils import configclass
+from isaaclab.actuators import ImplicitActuatorCfg
+from isaaclab import sim as sim_utils
+import os
+
+_DEMO_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+T1_USD_PATH = os.path.join(_DEMO_DIR, "asset", "t1", "t1_locomotion_physics.usd")
+
+@configclass
+class T1SceneCfg(InteractiveSceneCfg):
+    """T1 机器人的场景配置，包含地面、机器人和光照"""
+
+    # T1 机器人配置
+    # 注意：你需要将 usd_path 替换为你真实的 T1 机器人 USD 文件路径
+    robot = ArticulationCfg(
+        prim_path="{ENV_REGEX_NS}/Robot",
+        spawn=sim_utils.UsdFileCfg(
+            usd_path=T1_USD_PATH,
+            activate_contact_sensors=True,
+            rigid_props=sim_utils.RigidBodyPropertiesCfg(
+                disable_gravity=False,
+                retain_accelerations=False,
+                linear_damping=0.0,
+                angular_damping=0.0,
+                max_linear_velocity=1000.0,
+                max_angular_velocity=1000.0,
+                max_depenetration_velocity=1.0,
+            ),
+            articulation_props=sim_utils.ArticulationRootPropertiesCfg(
+                enabled_self_collisions=True,
+                solver_position_iteration_count=4,
+                solver_velocity_iteration_count=0
+            ),
+        ),
+        init_state=ArticulationCfg.InitialStateCfg(
+            pos=(0.0, 0.0, 0.7), # 初始高度，确保机器人双脚着地而非穿模
+            joint_pos={".*": 0.0}, # 所有关节初始角度为 0
+        ),
+        actuators={
+            "legs": ImplicitActuatorCfg(
+                joint_names_expr=[".*"], # 匹配所有关节，也可以指定具体名称如 ["L_Hip.*", "R_Hip.*"]
+                effort_limit=400.0,
+                velocity_limit=10.0,
+                stiffness=85.0, # P 增益
+                damping=2.0,    # D 增益
+            ),
+        },
+    )

rl_game/demo/train.py

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+import sys
+import os
+import argparse
+
+# 确保能找到项目根目录下的模块
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from isaaclab.app import AppLauncher
+
+# 1. 配置启动参数
+parser = argparse.ArgumentParser(description="Train T1 robot to Get-Up with RL-Games.")
+parser.add_argument("--num_envs", type=int, default=16384, help="起身任务建议并行 4096 即可")
+parser.add_argument("--task", type=str, default="Isaac-T1-GetUp-v0", help="任务 ID")
+parser.add_argument("--seed", type=int, default=42, help="随机种子")
+AppLauncher.add_app_launcher_args(parser)
+args_cli = parser.parse_args()
+
+# 2. 启动仿真器（必须在导入其他 isaaclab 模块前）
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+import torch
+import gymnasium as gym
+import yaml
+from isaaclab_rl.rl_games import RlGamesVecEnvWrapper
+from rl_games.torch_runner import Runner
+from rl_games.common import env_configurations, vecenv
+
+# 导入你刚刚修改好的配置类
+# 假设你的文件名是 t1_getup_cfg.py，类名是 T1EnvCfg
+from config.t1_env_cfg import T1EnvCfg
+
+# 3. 注册环境
+gym.register(
+    id="Isaac-T1-Walk-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv",
+    kwargs={
+        "cfg": T1EnvCfg(),  # 这里会加载你设置的随机旋转、时间惩罚等
+    },
+)
+
+
+def main():
+    # --- 新增：处理 Retrain 参数 ---
+    # 你可以手动指定路径，或者在 argparse 里增加一个 --checkpoint 参数
+    checkpoint_path = os.path.join(os.path.dirname(__file__), "logs/T1_GetUp/nn/**.pth")
+    # 检查模型文件是否存在
+    should_retrain = os.path.exists(checkpoint_path)
+
+    env = gym.make("Isaac-T1-Walk-v0", num_envs=args_cli.num_envs)
+
+    # 注意：rl_device 必须设置为 args_cli.device (通常是 'cuda:0')
+    wrapped_env = RlGamesVecEnvWrapper(
+        env,
+        rl_device=args_cli.device,
+        clip_obs=5.0,
+        clip_actions=1.0
+    )
+
+    vecenv.register('as_is', lambda config_name, num_actors, **kwargs: wrapped_env)
+
+    env_configurations.register('rlgym', {
+        'vecenv_type': 'as_is',
+        'env_creator': lambda **kwargs: wrapped_env
+    })
+
+    config_path = os.path.join(os.path.dirname(__file__), "config", "ppo_cfg.yaml")
+    with open(config_path, "r") as f:
+        rl_config = yaml.safe_load(f)
+
+    # 设置日志和实验名称
+    rl_game_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "."))
+    log_dir = os.path.join(rl_game_dir, "logs")
+    rl_config['params']['config']['train_dir'] = log_dir
+    rl_config['params']['config']['name'] = "T1_GetUp"
+
+    # --- 关键修改：注入模型路径 ---
+    if should_retrain:
+        print(f"[INFO]: 检测到预训练模型，正在从 {checkpoint_path} 恢复训练...")
+        # rl_games 会读取 config 中的 load_path 进行续训
+        rl_config['params']['config']['load_path'] = checkpoint_path
+    else:
+        print("[INFO]: 未找到预训练模型，将从零开始训练。")
+
+    # 7. 运行训练
+    runner = Runner()
+    runner.load(rl_config)
+
+    runner.run({
+        "train": True,
+        "play": False,
+        # 如果你想强制从某个 checkpoint 开始，也可以在这里传参
+        "checkpoint": checkpoint_path if should_retrain else None,
+        "vec_env": wrapped_env
+    })
+
+    simulation_app.close()
+
+
+if __name__ == "__main__":
+    main()