The demo of get up

rl_game/get_up/__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-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
+        fixed_sigma: True
+    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: 16384 # 同时训练的机器人数量
+    reward_shaper:
+      scale_value: 1.0
+    normalize_advantage: True
+    gamma: 0.96
+    tau: 0.95
+    learning_rate: 5e-4
+    lr_schedule: adaptive
+    kl_threshold: 0.013
+    score_to_win: 20000
+    max_epochs: 5000
+    save_best_after: 50
+    save_frequency: 100
+    grad_norm: 1.0
+    entropy_coef: 0.02
+    truncate_grads: True
+    bounds_loss_coef: 0.001
+    e_clip: 0.2
+    horizon_length: 32
+    minibatch_size: 16384
+    mini_epochs: 4
+    critic_coef: 2
+    clip_value: True

rl_game/get_up/config/t1_env_cfg.py

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+from isaaclab.assets import ArticulationCfg
+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.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
+
+import isaaclab.envs.mdp as mdp
+
+
+@configclass
+class T1ObservationCfg:
+    """观察值：必须包含姿态和高度，否则机器人不知道自己是怎么躺着的"""
+
+    @configclass
+    class PolicyCfg(ObsGroup):
+        concatenate_terms = True
+        enable_corruption = False
+
+        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)
+        # ⬅️ 新增：让机器人知道自己离地多高
+        root_height = ObsTerm(func=mdp.root_height)
+        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 T1EventCfg:
+    """事件配置：实现多种随机躺下姿态的关键"""
+
+    # 每次重置时，随机给基座一个旋转角度
+    # 包括：背躺 (Pitch=1.57), 趴着 (Pitch=-1.57), 侧躺 (Roll=1.57)
+    reset_robot_rotation = EventTerm(
+        func=mdp.reset_root_custom_orientation,
+        params={
+            "euler_angles_range": {
+                "roll": (-1.57, 1.57),  # 覆盖侧躺
+                "pitch": (-1.57, 1.57),  # 覆盖趴下和背躺
+                "yaw": (-3.14, 3.14),  # 随机朝向
+            },
+            "asset_cfg": SceneEntityCfg("robot"),
+        },
+        mode="reset",
+    )
+
+
+@configclass
+class T1GetUpRewardCfg:
+    """起身任务专用奖励：快、直、稳"""
+
+    # 1. 核心进度：高度奖励
+    # 只要低于目标高度(0.6m)，就会持续根据距离给负分，越高分数越接近0
+    height_progress = RewTerm(
+        func=mdp.root_height_below_minimum,
+        weight=15.0,
+        params={"minimum_height": 0.65}
+    )
+
+    # 2. 速度惩罚：时间就是金钱
+    # 每一帧都扣 0.5 分，起身越慢，被扣掉的总分就越多
+    time_penalty = RewTerm(
+        func=mdp.is_alive,
+        weight=-0.5
+    )
+
+    # 3. 姿态奖励：上半身必须垂直向上
+    upright = RewTerm(func=mdp.flat_orientation_l2, weight=2.0)
+
+    # 4. 平滑惩罚：防止起身时由于急躁导致的剧烈抖动
+    action_rate = RewTerm(func=mdp.action_rate_l2, weight=-0.01)
+    joint_vel = RewTerm(func=mdp.joint_vel_l2, weight=-0.001)
+
+
+@configclass
+class T1TerminationsCfg:
+    """终止条件"""
+    # 提前成功：如果站起来了并保持稳定，直接结束 Episode 并拿奖励，节省算力
+    is_standing_success = DoneTerm(
+        func=mdp.root_height_below_minimum,
+        params={"minimum_height": 0.65, "inverted": True}  # 高于 0.65m 则重置
+    )
+    time_out = DoneTerm(func=mdp.time_out)
+
+
+@configclass
+class T1EnvCfg(ManagerBasedRLEnvCfg):
+    """主环境配置：针对 T1 起身任务"""
+    scene = T1SceneCfg(num_envs=16384, env_spacing=2.5)
+
+    # 覆盖初始位置：必须低高度生成，防止随机旋转后高空坠落
+    def __post_init__(self):
+        super().__post_init__()
+        self.scene.robot.init_state.pos = (0.0, 0.0, 0.2)
+
+    observations = T1ObservationCfg()
+    rewards = T1GetUpRewardCfg()
+    terminations = T1TerminationsCfg()
+    events = T1EventCfg()  # ⬅️ 挂载随机姿态事件
+
+    actions = JointPositionActionCfg(
+        asset_name="robot", joint_names=[".*"], scale=0.5, use_default_offset=True
+    )
+
+    # 训练参数
+    episode_length_s = 5.0  # 强制 5秒内必须站起来
+    decimation = 4  # 提高控制频率 (约 50Hz) 有助于精细动作

rl_game/get_up/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):
+    """修改后的 T1 机器人场景配置，适配随机姿态起身任务"""
+
+    # 1. 地面配置 (如果之前没定义，必须加上)
+    ground = AssetBaseCfg(
+        prim_path="/World/ground",
+        spawn=sim_utils.GroundPlaneCfg(),
+    )
+
+    # 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, # ⬅️ 提高穿透修正速度，防止肢体卡在地下
+            ),
+            articulation_props=sim_utils.ArticulationRootPropertiesCfg(
+                enabled_self_collisions=True,    # 开启自碰撞，起身时肢体可能互碰
+                solver_position_iteration_count=8, # ⬅️ 增加迭代次数，提高物理稳定性
+                solver_velocity_iteration_count=4,
+            ),
+        ),
+        init_state=ArticulationCfg.InitialStateCfg(
+            # ⬅️ 核心修改：高度降低。因为是躺着生成，0.2m 比较合适
+            pos=(0.0, 0.0, 0.2),
+            # 默认旋转设为单位阵，具体的随机化由 Event 管理器处理
+            rot=(1.0, 0.0, 0.0, 0.0),
+            joint_pos={".*": 0.0},
+        ),
+        actuators={
+            "legs": ImplicitActuatorCfg(
+                joint_names_expr=[".*"],
+                effort_limit=400.0, # T1 通常有较强的力矩输出
+                velocity_limit=20.0, # ⬅️ 调高速度限制，起身需要爆发动作
+                stiffness=150.0,     # ⬅️ 调高刚度：起身需要更“硬”的支撑
+                damping=5.0,         # ⬅️ 增加阻尼：防止机器人站起瞬间晃动太厉害
+            ),
+        },
+    )
+
+    # 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),
+    )

rl_game/get_up/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-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():
+    # 4. 创建环境，显式传入命令行指定的 num_envs
+    env = gym.make("Isaac-T1-GetUp-v0", num_envs=args_cli.num_envs)
+
+    # 5. 包装环境
+    # 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  # 动作裁剪建议设小一点，防止电机输出瞬间爆表
+    )
+
+    # 注册给 rl_games 使用
+    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
+    })
+
+    # 6. 加载 PPO 配置文件
+    # 提示：由于是起身任务，建议在 ppo_cfg.yaml 中调大 mini_batch 大数或提高学习率
+    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_Experiment"
+
+    # 7. 运行训练
+    runner = Runner()
+    runner.load(rl_config)
+
+    print(f"[INFO]: 开始训练任务 {args_cli.task}，环境数量: {args_cli.num_envs}")
+
+    runner.run({
+        "train": True,
+        "play": False,
+        "vec_env": wrapped_env
+    })
+
+    simulation_app.close()
+
+
+if __name__ == "__main__":
+    main()