reward modification and change the get_up logic

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
+# 关键：确保当前目录在 sys.path 中，这样才能直接 from config 导入
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+import argparse
+from isaaclab.app import AppLauncher
+
+# 添加启动参数
+parser = argparse.ArgumentParser(description="Train T1 robot with rl_games.")
+parser.add_argument("--num_envs", type=int, default=16384, help="Number of envs to run.")
+AppLauncher.add_app_launcher_args(parser)
+args_cli = parser.parse_args()
+
+# 启动仿真器
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+import torch
+import gymnasium as gym
+from isaaclab_rl.rl_games import RlGamesVecEnvWrapper
+from rl_games.torch_runner import Runner
+import yaml
+from config.t1_env_cfg import T1EnvCfg
+from rl_games.common import env_configurations, vecenv
+
+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(),
+    },
+)
+
+def main():
+    # 1. 创建环境 (保持不变)
+    env = gym.make("Isaac-T1-Walking-v0", num_envs=args_cli.num_envs)
+
+    # 2. 包装环境 (保持不变)
+    wrapped_env = RlGamesVecEnvWrapper(
+        env,
+        rl_device=args_cli.device,
+        clip_obs=5.0,
+        clip_actions=100.0
+    )
+
+    vecenv.register('as_is', lambda config_name, num_actors, **kwargs: wrapped_env)
+
+    # 注册环境配置
+    env_configurations.register('rlgym', {
+        'vecenv_type': 'as_is',
+        'env_creator': lambda **kwargs: wrapped_env
+    })
+
+    # 3. 加载 PPO 配置 (保持不变)
+    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)
+
+    # 设置日志路径
+    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
+
+    # 4. 启动训练
+    runner = Runner()
+
+    # 此时 rl_config 只有文本和数字，没有复杂对象，deepcopy 会成功
+    runner.load(rl_config)
+
+    # 在 run 时传入对象是安全的
+    runner.run({
+        "train": True,
+        "play": False,
+        "vec_env": wrapped_env
+    })
+
+    simulation_app.close()
+
+# PYTHONPATH=. python rl_game/your_file_name/train.py
+
+if __name__ == "__main__":
+    main()

rl_game/get_up/__init__.py

@@ -0,0 +1,13 @@
+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-GetUp-v0",
+    entry_point="isaaclab.envs:ManagerBasedRLEnv", # Isaac Lab 统一的强化学习环境入口
+    kwargs={
+        "cfg": T1EnvCfg(),
+    },
+)

rl_game/get_up/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: 0.5
+        fixed_sigma: False
+    mlp:
+      units: [512, 256, 128]
+      activation: relu
+      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: 8192 # 同时训练的机器人数量
+    reward_shaper:
+      scale_value: 1.0
+    normalize_advantage: True
+    gamma: 0.98
+    tau: 0.95
+    learning_rate: 3e-4
+    lr_schedule: adaptive
+    kl_threshold: 0.015
+    score_to_win: 20000
+    max_epochs: 500
+    save_best_after: 50
+    save_frequency: 100
+    grad_norm: 1.0
+    entropy_coef: 0.005
+    truncate_grads: True
+    bounds_loss_coef: 0.001
+    e_clip: 0.2
+    horizon_length: 256
+    minibatch_size: 65536
+    mini_epochs: 4
+    critic_coef: 1
+    clip_value: True

rl_game/get_up/config/t1_env_cfg.py

@@ -0,0 +1,314 @@
+import torch
+import random
+import numpy as np
+import isaaclab.envs.mdp as mdp
+from isaaclab.assets import ArticulationCfg
+from isaaclab.envs import ManagerBasedRLEnvCfg, ManagerBasedRLEnv
+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.managers import EventTermCfg as EventTerm
+from isaaclab.envs.mdp import JointPositionActionCfg
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.utils import configclass
+from rl_game.get_up.env.t1_env import T1SceneCfg
+
+
+# --- 1. 自定义 MDP 逻辑函数 ---
+
+def standing_with_feet_reward(
+        env: ManagerBasedRLEnv,
+        min_head_height: float,
+        min_pelvis_height: float,
+        sensor_cfg: SceneEntityCfg,
+        force_threshold: float = 20.0,
+        max_v_z: float = 0.5
+) -> torch.Tensor:
+    """终极高度目标：头高、盆骨高、足部受力稳定"""
+    head_idx, _ = env.scene["robot"].find_bodies("H2")
+    pelvis_idx, _ = env.scene["robot"].find_bodies("Trunk")
+
+    curr_head_h = env.scene["robot"].data.body_state_w[:, head_idx[0], 2]
+    curr_pelvis_h = env.scene["robot"].data.body_state_w[:, pelvis_idx[0], 2]
+
+    # 归一化高度评分
+    head_score = torch.clamp(curr_head_h / min_head_height, 0.0, 1.2)
+    pelvis_score = torch.clamp(curr_pelvis_h / min_pelvis_height, 0.0, 1.2)
+    height_reward = (head_score + pelvis_score) / 2.0
+
+    # 足部受力判定
+    contact_sensor = env.scene.sensors.get(sensor_cfg.name)
+    if contact_sensor is None: return torch.zeros(env.num_envs, device=env.device)
+
+    foot_forces_z = torch.sum(contact_sensor.data.net_forces_w[:, :, 2], dim=-1)
+    force_weight = torch.sigmoid((foot_forces_z - force_threshold) / 5.0)
+
+    # 垂直速度惩罚（防止跳跃不稳）
+    root_vel_z = env.scene["robot"].data.root_lin_vel_w[:, 2]
+    vel_penalty = torch.exp(-torch.abs(root_vel_z) / max_v_z)
+
+    return height_reward * (0.5 + 0.5 * force_weight * vel_penalty)
+
+
+def dynamic_getup_strategy_reward(env: ManagerBasedRLEnv) -> torch.Tensor:
+    """
+    全姿态对称起立策略：
+    1. 核心蜷缩 (Spring Loading)：无论仰卧还是俯卧，只要高度低，就必须强制收腿。
+    2. 仰卧支撑 (Back-Pushing)：在仰卧状态下，鼓励手臂向后发力并抬高盆骨。
+    3. 协同爆发 (Explosive Jump)：蜷缩状态下产生的向上动量获得最高倍率奖励。
+    """
+    # --- 1. 获取物理状态 ---
+    gravity_z = env.scene["robot"].data.projected_gravity_b[:, 2]  # 1:仰卧, -1:俯卧
+    pelvis_idx, _ = env.scene["robot"].find_bodies("Trunk")
+    curr_pelvis_h = env.scene["robot"].data.body_state_w[:, pelvis_idx[0], 2]
+    root_vel_z = env.scene["robot"].data.root_lin_vel_w[:, 2]
+
+    # 关节索引：11,12髋, 17,18膝 (确保与T1模型一致)
+    knee_joints = [17, 18]
+    hip_pitch_joints = [11, 12]
+    joint_pos = env.scene["robot"].data.joint_pos
+
+    # --- 2. 核心蜷缩评分 (Crouch Score) ---
+    # 无论仰俯，蜷缩是起立的绝对前提。目标是让脚尽可能靠近质心。
+    # 提高膝盖弯曲目标 (1.5 rad)，引导更深度的折叠
+    knee_flex_err = torch.abs(joint_pos[:, knee_joints] - 1.5).sum(dim=-1)
+    hip_flex_err = torch.abs(joint_pos[:, hip_pitch_joints] - 1.2).sum(dim=-1)
+    crouch_score = torch.exp(-(knee_flex_err + hip_flex_err) * 0.6)
+
+    # 基础蜷缩奖励 (Spring Base) - 权重加大
+    crouch_trigger = torch.clamp(0.6 - curr_pelvis_h, min=0.0)
+    base_crouch_reward = crouch_trigger * crouch_score * 40.0
+
+    # --- 3. 支撑力奖励 (Support Force) ---
+    push_reward = torch.zeros_like(curr_pelvis_h)
+    contact_sensor = env.scene.sensors.get("contact_sensor")
+    if contact_sensor is not None:
+        # 监测非足部Link（手、臂）的受力
+        # 无论正反，只要手能提供垂直向上的推力，就是好手
+        arm_forces_z = contact_sensor.data.net_forces_w[:, :, 2]
+        push_reward = torch.tanh(torch.max(arm_forces_z, dim=-1)[0] / 30.0)
+
+    # --- 4. 姿态特定引导 (Orientation-Neutral) ---
+    is_back = torch.clamp(gravity_z, min=0.0)  # 仰卧程度
+    is_belly = torch.clamp(-gravity_z, min=0.0)  # 俯卧程度
+
+    # A. 仰卧直接起立逻辑：
+    # 在仰卧时，如果能把盆骨撑起来 (curr_pelvis_h 增加)，给予重奖
+    # 配合crouch_score，鼓励“收腿-撑地-挺髋”的动作链
+    back_lift_reward = is_back * torch.clamp(curr_pelvis_h - 0.15, min=0.0) * crouch_score * 50.0
+
+    # B. 俯卧/翻身辅助逻辑 (保留一定的翻身倾向，但不再是唯一路径)
+    flip_reward = is_back * (1.0 - gravity_z) * 5.0  # 权重降低，仅作为备选
+
+    # --- 5. 最终爆发项 (The Jump) ---
+    # 核心公式：蜷缩程度 * 向上速度 * 支撑力感应
+    # 这是一个通用的“起跳”奖励，无论正反面，只要满足“缩得紧、跳得快、手有撑”，奖励就爆炸
+    explosion_reward = crouch_score * torch.clamp(root_vel_z, min=0.0) * (0.5 + 0.5 * push_reward) * 80.0
+
+    # --- 6. 汇总 ---
+    total_reward = (
+            base_crouch_reward +  # 必须缩腿
+            back_lift_reward +  # 仰卧挺髋
+            flip_reward +  # 翻身尝试
+            explosion_reward  # 终极爆发
+    )
+
+    return total_reward
+
+def is_standing_still(
+        env: ManagerBasedRLEnv,
+        min_head_height: float,
+        min_pelvis_height: float,
+        max_angle_error: float,
+        standing_time: float,
+        velocity_threshold: float = 0.15
+) -> torch.Tensor:
+    head_idx, _ = env.scene["robot"].find_bodies("H2")
+    pelvis_idx, _ = env.scene["robot"].find_bodies("Trunk")
+
+    current_head_h = env.scene["robot"].data.body_state_w[:, head_idx[0], 2]
+    current_pelvis_h = env.scene["robot"].data.body_state_w[:, pelvis_idx[0], 2]
+
+    gravity_error = torch.norm(env.scene["robot"].data.projected_gravity_b[:, :2], dim=-1)
+    root_vel_norm = torch.norm(env.scene["robot"].data.root_lin_vel_w, dim=-1)
+
+    is_stable_now = (
+            (current_head_h > min_head_height) &
+            (current_pelvis_h > min_pelvis_height) &
+            (gravity_error < max_angle_error) &
+            (root_vel_norm < velocity_threshold)
+    )
+
+    if "stable_timer" not in env.extras:
+        env.extras["stable_timer"] = torch.zeros(env.num_envs, device=env.device)
+
+    dt = env.physics_dt * env.cfg.decimation
+    env.extras["stable_timer"] = torch.where(is_stable_now, env.extras["stable_timer"] + dt,
+                                             torch.zeros_like(env.extras["stable_timer"]))
+
+    return env.extras["stable_timer"] > standing_time
+
+
+# --- 2. 配置类 ---
+
+T1_JOINT_NAMES = [
+    'AAHead_yaw', 'Head_pitch',
+    'Left_Shoulder_Pitch', 'Left_Shoulder_Roll', 'Left_Elbow_Pitch', 'Left_Elbow_Yaw',
+    'Right_Shoulder_Pitch', 'Right_Shoulder_Roll', 'Right_Elbow_Pitch', 'Right_Elbow_Yaw',
+    'Waist',
+    'Left_Hip_Pitch', 'Right_Hip_Pitch', 'Left_Hip_Roll', 'Right_Hip_Roll',
+    'Left_Hip_Yaw', 'Right_Hip_Yaw', 'Left_Knee_Pitch', 'Right_Knee_Pitch',
+    'Left_Ankle_Pitch', 'Right_Ankle_Pitch', 'Left_Ankle_Roll', 'Right_Ankle_Roll'
+]
+
+
+@configclass
+class T1ObservationCfg:
+    @configclass
+    class PolicyCfg(ObsGroup):
+        concatenate_terms = True
+        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)
+        root_pos = ObsTerm(func=mdp.root_pos_w)
+        joint_pos = ObsTerm(func=mdp.joint_pos_rel,
+                            params={"asset_cfg": SceneEntityCfg("robot", joint_names=T1_JOINT_NAMES)})
+        joint_vel = ObsTerm(func=mdp.joint_vel_rel,
+                            params={"asset_cfg": SceneEntityCfg("robot", joint_names=T1_JOINT_NAMES)})
+        actions = ObsTerm(func=mdp.last_action)
+
+    policy = PolicyCfg()
+
+
+@configclass
+class T1EventCfg:
+    reset_robot_rotation = EventTerm(
+        func=mdp.reset_root_state_uniform,
+        params={
+            "asset_cfg": SceneEntityCfg("robot"),
+            "pose_range": {
+                "roll": (-1.57, 1.57),
+                "pitch": tuple(np.array([1.4, 1.6], dtype=np.float32) * random.choice([-1 , 1])),
+                "yaw": (-3.14, 3.14),
+                "x": (0.0, 0.0),
+                "y": (0.0, 0.0),
+                "z": (0.3, 0.4),
+            },
+            "velocity_range": {},
+        },
+        mode="reset",
+    )
+
+
+@configclass
+class T1ActionCfg:
+    # 拆分动作组以防止抽搐。由于不强制规定动作，我们可以给各个部位较为均衡的探索范围。
+    arm_action = JointPositionActionCfg(
+        asset_name="robot",
+        joint_names=[
+            'Left_Shoulder_Pitch', 'Left_Shoulder_Roll', 'Left_Elbow_Pitch', 'Left_Elbow_Yaw',
+            'Right_Shoulder_Pitch', 'Right_Shoulder_Roll', 'Right_Elbow_Pitch', 'Right_Elbow_Yaw'
+        ],
+        scale=1.2,  # 给了手臂相对充裕的自由度去摸索
+        use_default_offset=True
+    )
+
+    torso_action = JointPositionActionCfg(
+        asset_name="robot",
+        joint_names=['Waist', 'AAHead_yaw', 'Head_pitch'],
+        scale=0.8,
+        use_default_offset=True
+    )
+
+    leg_action = JointPositionActionCfg(
+        asset_name="robot",
+        joint_names=[
+            'Left_Hip_Pitch', 'Right_Hip_Pitch', 'Left_Hip_Roll', 'Right_Hip_Roll',
+            'Left_Hip_Yaw', 'Right_Hip_Yaw', 'Left_Knee_Pitch', 'Right_Knee_Pitch',
+            'Left_Ankle_Pitch', 'Right_Ankle_Pitch', 'Left_Ankle_Roll', 'Right_Ankle_Roll'
+        ],
+        scale=0.6,
+        use_default_offset=True
+    )
+
+
+@configclass
+class T1GetUpRewardCfg:
+    # 1. 核心阶段性引导 (翻身 -> 蜷缩 -> 支撑)
+    dynamic_strategy = RewTerm(
+        func=dynamic_getup_strategy_reward,
+        weight=1.5
+    )
+
+    # 2. 站立质量奖励 (强化双脚受力)
+    height_with_feet = RewTerm(
+        func=standing_with_feet_reward,
+        weight=40.0,  # 大权重
+        params={
+            "min_head_height": 1.1,
+            "min_pelvis_height": 0.7,
+            "sensor_cfg": SceneEntityCfg("contact_sensor", body_names=[".*_foot_link"]),
+            "force_threshold": 40.0,  # 必须达到一定压力，防止脚尖点地作弊
+            "max_v_z": 0.2
+        }
+    )
+
+    # 3. 惩罚项：防止钻空子
+    # 严厉惩罚：如果躯干(Trunk)或头(H2)直接接触地面，扣大分
+    body_contact_penalty = RewTerm(
+        func=mdp.contact_forces,
+        weight=-20.0,
+        params={
+            "sensor_cfg": SceneEntityCfg("contact_sensor", body_names=["Trunk", "H2"]),
+            "threshold": 1.0
+        }
+    )
+
+    # 4. 关节功耗惩罚 (防止高频抽搐)
+    action_rate = RewTerm(
+        func=mdp.action_rate_l2,
+        weight=-0.01
+    )
+
+    # 5. 成功维持奖励
+    is_success_maintain = RewTerm(
+        func=is_standing_still,
+        weight=1000.0,  # 巨大的成功奖励
+        params={
+            "min_head_height": 1.08,
+            "min_pelvis_height": 0.72,
+            "max_angle_error": 0.2,
+            "standing_time": 0.4,  # 必须站稳 0.4s
+            "velocity_threshold": 0.3
+        }
+    )
+
+
+@configclass
+class T1GetUpTerminationsCfg:
+    time_out = DoneTerm(func=mdp.time_out)
+    standing_success = DoneTerm(
+        func=is_standing_still,
+        params={
+            "min_head_height": 1.05,
+            "min_pelvis_height": 0.75,
+            "max_angle_error": 0.3,
+            "standing_time": 0.2,
+            "velocity_threshold": 0.5
+        }
+    )
+
+
+@configclass
+class T1EnvCfg(ManagerBasedRLEnvCfg):
+    scene = T1SceneCfg(num_envs=8192, env_spacing=2.5)
+
+    observations = T1ObservationCfg()
+    rewards = T1GetUpRewardCfg()
+    terminations = T1GetUpTerminationsCfg()
+    events = T1EventCfg()
+    actions = T1ActionCfg()
+
+    episode_length_s = 10.0
+    decimation = 4

rl_game/get_up/env/t1_env.py

@@ -0,0 +1,74 @@
+from isaaclab.assets import ArticulationCfg, AssetBaseCfg
+from isaaclab.scene import InteractiveSceneCfg
+from isaaclab.sensors import ContactSensorCfg
+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_lab.usd")
+
+@configclass
+class T1SceneCfg(InteractiveSceneCfg):
+    """最终修正版：彻底解决 Unknown asset config type 报错"""
+
+    # 1. 地面配置：直接在 spawn 内部定义材质
+    ground = AssetBaseCfg(
+        prim_path="/World/ground",
+        spawn=sim_utils.GroundPlaneCfg(
+            physics_material=sim_utils.RigidBodyMaterialCfg(
+                static_friction=1.0,
+                dynamic_friction=1.0,
+                restitution=0.3,
+                friction_combine_mode="average",
+                restitution_combine_mode="average",
+            )
+        ),
+    )
+
+    # 2. 机器人配置
+    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,
+                max_depenetration_velocity=10.0,
+            ),
+            articulation_props=sim_utils.ArticulationRootPropertiesCfg(
+                enabled_self_collisions=True,
+                solver_position_iteration_count=8,
+                solver_velocity_iteration_count=4,
+            ),
+        ),
+        init_state=ArticulationCfg.InitialStateCfg(
+            pos=(0.0, 0.0, 0.4), # 掉落高度
+            joint_pos={".*": 0.0},
+        ),
+        actuators={
+            "t1_joints": ImplicitActuatorCfg(
+                joint_names_expr=[".*"],
+                effort_limit=800.0,  # 翻倍，确保电机有力气
+                velocity_limit=20.0,
+                stiffness=500.0,  # 【关键】从 150 提到 500-800 之间
+                damping=40.0,  # 【关键】从 5 提到 30-50 之间，抑制乱抖
+            ),
+        },
+    )
+
+    contact_sensor = ContactSensorCfg(
+        prim_path="{ENV_REGEX_NS}/Robot/.*",
+        update_period=0.0,
+        history_length=3,
+    )
+
+    # 3. 光照配置
+    light = AssetBaseCfg(
+        prim_path="/World/light",
+        spawn=sim_utils.DistantLightCfg(color=(0.75, 0.75, 0.75), intensity=3000.0),
+    )
+
+# ['Trunk', 'H1', 'H2', 'AL1', 'AL2', 'AL3', 'left_hand_link', 'AR1', 'AR2', 'AR3', 'right_hand_link', 'Waist', 'Hip_Pitch_Left', 'Hip_Roll_Left', 'Hip_Yaw_Left', 'Shank_Left', 'Ankle_Cross_Left', 'left_foot_link', 'Hip_Pitch_Right', 'Hip_Roll_Right', 'Hip_Yaw_Right', 'Shank_Right', 'Ankle_Cross_Right', 'right_foot_link']

rl_game/get_up/train.py

@@ -0,0 +1,101 @@
+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=8192, 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-GetUp-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/T1_GetUp.pth")
+    # 检查模型文件是否存在
+    should_retrain = os.path.exists(checkpoint_path)
+
+    env = gym.make("Isaac-T1-GetUp-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()