diff --git a/UPDATE.md b/UPDATE.md index 0647555..0dd41f2 100644 --- a/UPDATE.md +++ b/UPDATE.md @@ -1,6 +1,7 @@ # UPDATE -## 20260106 +## 20260103 ### v1.0.4 +1. 加入save_additional_output参数, 启动会自动记录每个step的latent和weights Fix bug: 修复高频评估会将之前的评估冲掉问题 ## 20260101 ### v1.0.3 diff --git a/robogauge/tasks/custom/go2/go2_flat_task.py b/robogauge/tasks/custom/go2/go2_flat_task.py index 1c598f4..7264f43 100644 --- a/robogauge/tasks/custom/go2/go2_flat_task.py +++ b/robogauge/tasks/custom/go2/go2_flat_task.py @@ -7,7 +7,6 @@ @Blog : https://wty-yy.github.io/ @Desc : Go2 Flat Task Configuration ''' -from robogauge.tasks.robots import Go2Config, Go2MoEConfig from robogauge.tasks.gauge import FlatGaugeConfig from robogauge.tasks.simulator.mujoco_config import MujocoConfig diff --git a/robogauge/tasks/custom/go2/go2_obstacle_task.py b/robogauge/tasks/custom/go2/go2_obstacle_task.py index 638a3a8..a2b3aa9 100644 --- a/robogauge/tasks/custom/go2/go2_obstacle_task.py +++ b/robogauge/tasks/custom/go2/go2_obstacle_task.py @@ -7,7 +7,6 @@ @Blog : https://wty-yy.github.io/ @Desc : Go2 Obstacle Task Configuration ''' -from robogauge.tasks.robots import Go2Config, Go2MoEConfig from robogauge.tasks.gauge import ObstacleGaugeConfig from robogauge.tasks.simulator.mujoco_config import MujocoConfig diff --git a/robogauge/tasks/custom/go2/go2_slope_task.py b/robogauge/tasks/custom/go2/go2_slope_task.py index 3b1169b..a3d47ab 100644 --- a/robogauge/tasks/custom/go2/go2_slope_task.py +++ b/robogauge/tasks/custom/go2/go2_slope_task.py @@ -7,7 +7,6 @@ @Blog : https://wty-yy.github.io/ @Desc : Go2 Slope Task Configuration ''' -from robogauge.tasks.robots import Go2Config, Go2MoEConfig from robogauge.tasks.gauge import SlopeForwardGaugeConfig, SlopeBackwardGaugeConfig from robogauge.tasks.simulator.mujoco_config import MujocoConfig diff --git a/robogauge/tasks/custom/go2/go2_wave_task.py b/robogauge/tasks/custom/go2/go2_wave_task.py index f80976a..c52cd16 100644 --- a/robogauge/tasks/custom/go2/go2_wave_task.py +++ b/robogauge/tasks/custom/go2/go2_wave_task.py @@ -1,5 +1,12 @@ - -from robogauge.tasks.robots import Go2Config, Go2MoEConfig +# -*- coding: utf-8 -*- +''' +@File : go2_wave_task.py +@Time : 2026/01/02 23:16:45 +@Author : wty-yy +@Version : 1.0 +@Blog : https://wty-yy.github.io/ +@Desc : Go2 Wave Task Configuration +''' from robogauge.tasks.gauge import WaveGaugeConfig from robogauge.tasks.simulator.mujoco_config import MujocoConfig diff --git a/robogauge/tasks/robots/base_robot_config.py b/robogauge/tasks/robots/base_robot_config.py index 9fa7664..b781077 100644 --- a/robogauge/tasks/robots/base_robot_config.py +++ b/robogauge/tasks/robots/base_robot_config.py @@ -37,6 +37,7 @@ class RobotConfig(Config): 0.1, 1.0, -1.5, -0.1, 1.0, -1.5] mj2model_dof_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + save_additional_output = False class scales: lin_vel = 2.0 diff --git a/robogauge/tasks/robots/go2/go2_config.py b/robogauge/tasks/robots/go2/go2_config.py index 068a2e0..36d6bd3 100644 --- a/robogauge/tasks/robots/go2/go2_config.py +++ b/robogauge/tasks/robots/go2/go2_config.py @@ -7,7 +7,7 @@ @Blog : https://wty-yy.github.io/ @Desc : Go2 Robot Configuration ''' -from typing_extensions import Literal +from typing import Literal from robogauge.tasks.robots import RobotConfig class Go2Config(RobotConfig): @@ -37,6 +37,7 @@ class Go2Config(RobotConfig): 0.1, 1.0, -1.5, -0.1, 1.0, -1.5] mj2model_dof_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + save_additional_output = False class scales(RobotConfig.control.scales): lin_vel = 2.0 diff --git a/robogauge/tasks/robots/go2/go2_moe.py b/robogauge/tasks/robots/go2/go2_moe.py index b75254d..1f124c9 100644 --- a/robogauge/tasks/robots/go2/go2_moe.py +++ b/robogauge/tasks/robots/go2/go2_moe.py @@ -9,20 +9,40 @@ ''' import torch import numpy as np +from collections import defaultdict from robogauge.tasks.robots.go2.go2 import Go2 +from robogauge.utils.logger import logger class Go2MoE(Go2): + def __init__(self, cfg): + super().__init__(cfg) + self.save_info = defaultdict(list) + self.save_count = 0 + def get_action(self, obs: np.ndarray): obs_tensor = torch.tensor(obs, dtype=torch.float32).unsqueeze(0).to(self.device) action, results = self.model(obs_tensor) - if isinstance(results, tuple): + if isinstance(results, tuple) and len(results) == 2: weights, latent = results - latent = latent.detach().cpu().numpy().squeeze(0) - else: - weights = results + latent = latent.detach().cpu().numpy().squeeze(0) if latent is not None else None + weights = weights.detach().cpu().numpy().squeeze(0) if weights is not None else None + if self.cfg.control.save_additional_output: + self.save_info['latent'].append(latent) + self.save_info['weights'].append(weights) action = action.detach().cpu().numpy().squeeze(0)[self.model2mj_idx] - weights = weights.detach().cpu().numpy().squeeze(0) self.last_action = action target_dof_pos = action * self.action_scale + self.default_dof_pos return target_dof_pos, self.p_gains, self.d_gains, self.control_type + + def reset(self): + super().reset() + save_path = logger.log_dir / f"moe_info_{self.save_count}.npz" + if self.cfg.control.save_additional_output: + np.savez_compressed(save_path, + weights=np.array(self.save_info['weights']), + latent=np.array(self.save_info['latent']) + ) + logger.info(f"Saved MoE info to {save_path}") + self.save_count += 1 + self.save_info = defaultdict(list) diff --git a/robogauge/tasks/robots/go2/go2_moe_config.py b/robogauge/tasks/robots/go2/go2_moe_config.py index 3de123c..89c9b4c 100644 --- a/robogauge/tasks/robots/go2/go2_moe_config.py +++ b/robogauge/tasks/robots/go2/go2_moe_config.py @@ -14,6 +14,7 @@ class Go2MoEConfig(Go2Config): class control(Go2Config.control): model_path = "{ROBOGAUGE_ROOT_DIR}/resources/models/go2/go2_moe_cts_124k.pt" + save_additional_output = False class Go2MoETerrainConfig(Go2MoEConfig): """ Go2 MoE Robot Configuration for Terrain Tasks (wave, stairs up/down, slope, obstacles) """ diff --git a/robogauge/utils/visualize/plot_latent_pca.py b/robogauge/utils/visualize/plot_latent_pca.py new file mode 100644 index 0000000..ce6ec88 --- /dev/null +++ b/robogauge/utils/visualize/plot_latent_pca.py @@ -0,0 +1,140 @@ +import numpy as np +import matplotlib.pyplot as plt +from sklearn.decomposition import PCA +import os +import glob + +# ================= 配置区域 ================= +data_root = "/root/Coding/RoboGauge/logs_latent" + +# 地形列表 (我们需要遍历所有地形来收集同一个指令的数据) +terrains = ['flat', 'wave', 'slope_fd', 'slope_bd', 'stairs_fd', 'stairs_bd', 'obstacle'] + +# 指令 ID 与含义的映射 (根据你的描述) +command_map = { + 0: "Pos X", + 1: "Neg X", + 2: "Pos Y", + 3: "Neg Y", + 4: "Pos Yaw", + 5: "Neg Yaw" +} + +# 颜色映射 (为6种指令分配不同颜色) +cmd_colors = { + 0: '#d62728', # 红 + 1: '#1f77b4', # 蓝 + 2: '#2ca02c', # 绿 + 3: '#ff7f0e', # 橙 + 4: '#9467bd', # 紫 + 5: '#8c564b' # 棕 +} + +# 每种指令最大保留样本数 +# 因为我们要把7个地形的数据合起来，数据量会很大，必须采样 +# 建议 1000 - 2000，太少看不出分布，太多会糊成一团 +MAX_SAMPLES_PER_CMD = 3000 + +# ================= 功能函数 ================= + +def load_combined_command_data(root_path, terrain_list, cmd_id): + """ + 遍历所有地形文件夹，寻找指定 cmd_id 的 npz 文件，并将它们全部合并 + """ + cmd_latents = [] + + filename = f"moe_info_{cmd_id}.npz" + + for t_name in terrain_list: + # 搜索路径: /root/.../go2_moe_{terrain}_latent/*/moe_info_{id}.npz + search_pattern = os.path.join(root_path, f"go2_moe_{t_name}_latent", "*", filename) + files = glob.glob(search_pattern) + + for f in files: + try: + data = np.load(f) + if 'latent' in data: + cmd_latents.append(data['latent']) + except: + pass + + if not cmd_latents: + return None + + # 合并该指令下所有地形的数据 + combined = np.concatenate(cmd_latents, axis=0) + + return combined + +# ================= 主程序 ================= + +print(f"Start processing. Grouping by COMMAND (0-5)...") + +# 1. 数据收集与预处理 +all_data = [] # 存放 latent 向量 +all_labels = [] # 存放对应的指令 ID (0, 1, 2...) + +for cmd_id, cmd_name in command_map.items(): + print(f" - Loading data for Command {cmd_id}: {cmd_name} ...", end=" ") + + # 获取该指令在所有地形下的数据汇总 + raw_data = load_combined_command_data(data_root, terrains, cmd_id) + + if raw_data is not None: + # 随机下采样 (防止数据量过大) + n_total = len(raw_data) + if n_total > MAX_SAMPLES_PER_CMD: + indices = np.random.choice(n_total, MAX_SAMPLES_PER_CMD, replace=False) + data_sample = raw_data[indices] + else: + data_sample = raw_data + + all_data.append(data_sample) + # 记录标签：有多少个数据，就存多少个 label + all_labels.extend([cmd_id] * len(data_sample)) + print(f"Got {len(data_sample)} samples (from {n_total})") + else: + print("No data found!") + +if not all_data: + print("Error: No data loaded.") + exit() + +# 将列表转换为大矩阵 +X = np.concatenate(all_data, axis=0) +y = np.array(all_labels) + +# 2. PCA 降维 +print(f"Running PCA on total {X.shape[0]} samples...") +pca = PCA(n_components=2) +X_2d = pca.fit_transform(X) + +# 3. 可视化绘制 +plt.figure(figsize=(10, 8), dpi=120) + +# 遍历 0-5 进行绘制 +for cmd_id in command_map.keys(): + # 提取属于当前指令的 2D 点 + indices = (y == cmd_id) + points = X_2d[indices] + + if len(points) > 0: + plt.scatter( + points[:, 0], + points[:, 1], + c=cmd_colors[cmd_id], + label=command_map[cmd_id], + alpha=0.6, # 透明度 + s=15 # 点大小 + ) + +plt.title("PCA of Latent Space grouped by Control Command (All Terrains Mixed)") +plt.xlabel("PC 1") +plt.ylabel("PC 2") +plt.legend(title="Control Commands", markerscale=1.5) +plt.grid(True, linestyle='--', alpha=0.4) + +save_path = 'latent_pca_by_command_mixed.png' +plt.savefig(save_path) +print(f"Done! Visualization saved to {save_path}") +plt.show() \ No newline at end of file diff --git a/robogauge/utils/visualize/plot_latent_tsne.py b/robogauge/utils/visualize/plot_latent_tsne.py new file mode 100644 index 0000000..ad3f2fc --- /dev/null +++ b/robogauge/utils/visualize/plot_latent_tsne.py @@ -0,0 +1,198 @@ +import numpy as np +import matplotlib.pyplot as plt +from sklearn.manifold import TSNE +import os +import glob +import pandas as pd +from sklearn.metrics import silhouette_score, silhouette_samples, calinski_harabasz_score + +# ================= 配置区域 ================= +data_root = "/root/Coding/RoboGauge/logs_latent/moe" +terrains = ['flat', 'wave', 'slope_fd', 'slope_bd', 'stairs_fd', 'stairs_bd', 'obstacle'] + +command_map = { + 0: "Pos X (Forward)", + 1: "Neg X (Backward)", + 2: "Pos Y (Left)", + 3: "Neg Y (Right)", + 4: "Pos Yaw (Turn Left)", + 5: "Neg Yaw (Turn Right)" +} + +cmd_colors = { + 0: '#d62728', 1: '#1f77b4', 2: '#2ca02c', + 3: '#ff7f0e', 4: '#9467bd', 5: '#8c564b' +} + +# t-SNE 计算较慢，且点太多会严重重叠 +# 建议每种指令采样 800 - 1000 个点即可，足以看清分布 +MAX_SAMPLES_PER_CMD = 2000 + +# t-SNE 参数 +# perplexity: 困惑度，通常在 5-50 之间。越大越关注全局结构，越小越关注局部邻居。 +TSNE_PERPLEXITY = 30 +TSNE_ITER = 1000 + +# ================= 功能函数 ================= + +# key = 'weights' # 'latent' +key = 'latent' + +def load_combined_command_data(root_path, terrain_list, cmd_id): + """读取指定指令在所有地形下的数据并合并""" + cmd_latents = [] + filename = f"moe_info_{cmd_id}.npz" + + for t_name in terrain_list: + search_pattern = os.path.join(root_path, f"go2_moe_{t_name}_latent", "*", filename) + files = glob.glob(search_pattern) + for f in files: + try: + data = np.load(f) + if key in data: + cmd_latents.append(data[key]) + except: pass + + if not cmd_latents: return None + return np.concatenate(cmd_latents, axis=0) + +# ================= 主程序 ================= + +print(f"Start processing t-SNE visualization...") + +all_data = [] +all_labels = [] + +# 1. 数据收集 +for cmd_id, cmd_name in command_map.items(): + raw_data = load_combined_command_data(data_root, terrains, cmd_id) + + if raw_data is not None: + # 随机下采样 + n_total = len(raw_data) + if n_total > MAX_SAMPLES_PER_CMD: + indices = np.random.choice(n_total, MAX_SAMPLES_PER_CMD, replace=False) + data_sample = raw_data[indices] + else: + data_sample = raw_data + + all_data.append(data_sample) + all_labels.extend([cmd_id] * len(data_sample)) + print(f" - Cmd {cmd_id}: {len(data_sample)} samples") + +if not all_data: + print("Error: No data loaded.") + exit() + +X = np.concatenate(all_data, axis=0) +y = np.array(all_labels) + +print(f"Running t-SNE on {X.shape} matrix...") +print(f" (Perplexity={TSNE_PERPLEXITY}, Iterations={TSNE_ITER})") +print(" This may take a moment...") + +# 2. t-SNE 降维 +# init='pca' 通常比 'random' 更稳定，能更好地保留全局结构 +tsne = TSNE( + n_components=2, + perplexity=TSNE_PERPLEXITY, + n_iter=TSNE_ITER, + init='pca', + learning_rate='auto', + random_state=42, + verbose=1, +) +X_embedded = tsne.fit_transform(X) + +# 3. 可视化 +plt.figure(figsize=(12, 10), dpi=120) + +for cmd_id in command_map.keys(): + indices = (y == cmd_id) + points = X_embedded[indices] + + if len(points) > 0: + plt.scatter( + points[:, 0], + points[:, 1], + c=cmd_colors[cmd_id], + label=command_map[cmd_id], + alpha=0.6, + s=20 #稍微大一点的点 + ) + +plt.title(f"t-SNE Visualization of {key.capitalize()} Space by Command\n(Perplexity={TSNE_PERPLEXITY}, Mixed Terrains)") +# t-SNE 的坐标轴没有物理意义，所以隐藏刻度通常更好看 +plt.xticks([]) +plt.yticks([]) +plt.legend(title="Control Commands", markerscale=2, bbox_to_anchor=(1.05, 1), loc='upper left') +plt.tight_layout() + +save_path = os.path.join(data_root, f'{key}_tsne_by_command.png') +plt.savefig(save_path) +print(f"Done! Visualization saved to {save_path}") +plt.show() + +def evaluate_latent_space(X, y, command_map): + """ + X: (N, D) 原始高维 Latent 向量 (例如 32维) + y: (N,) 对应的指令标签 (0-5) + command_map: label到名称的映射字典 + """ + print(f"--- Evaluating Latent Space (Samples: {X.shape[0]}, Dim: {X.shape[1]}) ---") + + # 1. 全局指标 (Global Metrics) + # 轮廓系数 (Silhouette): 越接近 1 越好 + global_sil = silhouette_score(X, y) + # Calinski-Harabasz Index (CHI): 数值越大越好 (表示类间离散度高，类内离散度低) + global_chi = calinski_harabasz_score(X, y) + + print(f"Global Silhouette Score: {global_sil:.4f} (范围 -1 到 1, 越大越好)") + print(f"Global Calinski-Harabasz: {global_chi:.1f} (数值越大越好)") + print("-" * 30) + + # 2. 逐类指标 (Per-Class Metrics) + # 计算每个样本的轮廓系数 + sample_silhouette_values = silhouette_samples(X, y) + + class_scores = [] + + for label in sorted(command_map.keys()): + # 提取当前类的所有样本的轮廓系数 + ith_class_silhouette_values = sample_silhouette_values[y == label] + + # 计算该类的平均分 + avg_score = np.mean(ith_class_silhouette_values) + + # 计算该类的紧密度 (Intra-class distance) - 可选 + # 这里直接用轮廓系数代表聚集程度 + + class_scores.append({ + "Command ID": label, + "Command Name": command_map[label], + "Silhouette Score": avg_score, + "Sample Count": len(ith_class_silhouette_values) + }) + + # 转为 DataFrame 展示 + df = pd.DataFrame(class_scores) + print(df.to_string(index=False)) + print("-" * 30 + "\n") + + return df, global_sil + +# 1. 评估原始高维空间 (Intrinsic Quality) +print(">>> Evaluating Original High-Dim Latent Space Quality...") +df_original, score_original = evaluate_latent_space(X, y, command_map) +print(f"Original Space Score: {score_original:.4f}") + +# 2. 评估 t-SNE 降维后的 2D 空间 (Visual Cluster Quality) +print("\n>>> Evaluating t-SNE Embedded Space Quality (2D)...") +# 直接传入 X_embedded 即可，evaluate_latent_space 函数对维度不敏感 +df_tsne, score_tsne = evaluate_latent_space(X_embedded, y, command_map) + +# 保存 t-SNE 的评估结果 +save_path_tsne = os.path.join(data_root, f'{key}_tsne_2d_evaluation.csv') +df_tsne.to_csv(save_path_tsne, index=False) +print(f"t-SNE 2D Evaluation results saved to {save_path_tsne}") +print(f"t-SNE Space Score: {score_tsne:.4f}") \ No newline at end of file diff --git a/run.bash b/scripts/run.bash similarity index 94% rename from run.bash rename to scripts/run.bash index 7708b72..2f08d48 100755 --- a/run.bash +++ b/scripts/run.bash @@ -7,7 +7,7 @@ python robogauge/scripts/run.py \ --experiment-name debug \ --stress-benchmark \ --stress-terrain-names flat slope_fd slope_bd stairs_fd stairs_bd wave obstacle \ - --num-processes 62 \ + --num-processes 35 \ --seeds 0 1 2 \ --search-seeds 0 1 2 3 4 \ --frictions 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25 2.5 \ @@ -20,7 +20,7 @@ python robogauge/scripts/run.py \ --experiment-name debug \ --stress-benchmark \ --stress-terrain-names flat slope_fd slope_bd stairs_fd stairs_bd wave obstacle \ - --num-processes 62 \ + --num-processes 35 \ --seeds 0 1 2 \ --search-seeds 0 1 2 3 4 \ --frictions 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25 2.5 \ @@ -33,7 +33,7 @@ python robogauge/scripts/run.py \ --experiment-name debug \ --stress-benchmark \ --stress-terrain-names flat slope_fd slope_bd stairs_fd stairs_bd wave obstacle \ - --num-processes 62 \ + --num-processes 35 \ --seeds 0 1 2 \ --search-seeds 0 1 2 3 4 \ --frictions 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25 2.5 \ @@ -45,7 +45,7 @@ python robogauge/scripts/run.py \ --experiment-name debug \ --stress-benchmark \ --stress-terrain-names flat slope_fd slope_bd stairs_fd stairs_bd wave obstacle \ - --num-processes 62 \ + --num-processes 35 \ --seeds 0 1 2 \ --search-seeds 0 1 2 3 4 \ --frictions 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25 2.5 \ @@ -58,7 +58,7 @@ python robogauge/scripts/run.py \ --experiment-name debug \ --stress-benchmark \ --stress-terrain-names flat slope_fd slope_bd stairs_fd stairs_bd wave obstacle \ - --num-processes 62 \ + --num-processes 35 \ --seeds 0 1 2 \ --search-seeds 0 1 2 3 4 \ --frictions 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25 2.5 \ @@ -71,7 +71,7 @@ python robogauge/scripts/run.py \ --experiment-name debug \ --stress-benchmark \ --stress-terrain-names flat slope_fd slope_bd stairs_fd stairs_bd wave obstacle \ - --num-processes 62 \ + --num-processes 35 \ --seeds 0 1 2 \ --search-seeds 0 1 2 3 4 \ --frictions 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25 2.5 \ diff --git a/scripts/run_save_latent.bash b/scripts/run_save_latent.bash new file mode 100755 index 0000000..75eb746 --- /dev/null +++ b/scripts/run_save_latent.bash @@ -0,0 +1,88 @@ +#!/bin/bash + +# Change robogauge/tasks/robots/go2/go2_moe_config.py `save_additional_output = True` +# This + +source /root/Programs/miniforge3/bin/activate robot + +# python robogauge/scripts/run.py \ +# --task-name go2_moe \ +# --model-path /root/Coding/RoboGauge/mytest/go2_moe_cts_111k_61.09%.pt \ +# --experiment-name debug \ +# --stress-benchmark \ +# --stress-terrain-names flat slope_fd slope_bd stairs_fd stairs_bd wave obstacle \ +# --num-processes 35 \ +# --seeds 0 1 2 \ +# --search-seeds 0 1 2 3 4 \ +# --frictions 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25 2.5 \ +# --compress-logs \ +# --headless + +python robogauge/scripts/run.py \ + --task-name go2_moe.flat \ + --model-path /root/Coding/RoboGauge/mytest/go2_moe_cts_111k_61.09%.pt \ + --experiment-name latent \ + --seed 0 \ + --friction 1.5 \ + --goals max_velocity \ + --headless + +python robogauge/scripts/run.py \ + --task-name go2_moe.obstacle \ + --level 9 \ + --model-path /root/Coding/RoboGauge/mytest/go2_moe_cts_111k_61.09%.pt \ + --experiment-name latent \ + --seed 0 \ + --friction 1.5 \ + --goals max_velocity \ + --headless + +python robogauge/scripts/run.py \ + --task-name go2_moe.slope_bd \ + --level 6 \ + --model-path /root/Coding/RoboGauge/mytest/go2_moe_cts_111k_61.09%.pt \ + --experiment-name latent \ + --seed 0 \ + --friction 1.5 \ + --goals max_velocity \ + --headless + +python robogauge/scripts/run.py \ + --task-name go2_moe.slope_fd \ + --level 6 \ + --model-path /root/Coding/RoboGauge/mytest/go2_moe_cts_111k_61.09%.pt \ + --experiment-name latent \ + --seed 0 \ + --friction 1.5 \ + --goals max_velocity \ + --headless + +python robogauge/scripts/run.py \ + --task-name go2_moe.stairs_bd \ + --level 7 \ + --model-path /root/Coding/RoboGauge/mytest/go2_moe_cts_111k_61.09%.pt \ + --experiment-name latent \ + --seed 0 \ + --friction 1.5 \ + --goals max_velocity \ + --headless + +python robogauge/scripts/run.py \ + --task-name go2_moe.stairs_fd \ + --level 9 \ + --model-path /root/Coding/RoboGauge/mytest/go2_moe_cts_111k_61.09%.pt \ + --experiment-name latent \ + --seed 0 \ + --friction 1.5 \ + --goals max_velocity \ + --headless + +python robogauge/scripts/run.py \ + --task-name go2_moe.wave \ + --level 6 \ + --model-path /root/Coding/RoboGauge/mytest/go2_moe_cts_111k_61.09%.pt \ + --experiment-name latent \ + --seed 0 \ + --friction 1.5 \ + --goals max_velocity \ + --headless