v0.1.10

README.md

@@ -28,9 +28,10 @@
 | - | - | - |
 | 电机动作执行随机延迟 | `action delay` | `<= RL控制间隔` |
 | base负重 | `base mass` | `-1, 0, 1, 2, 3 kg` |
+| 地面摩擦力 | `friction` | `0.4, 0.7, 1.0, 1.3, 1.6` |
 
 #### 地形
-1. 支持legged_gym中的部分地形, 包括: `wave, slope, rough_slope, stairs up, stairs down, obstacles, flat`, 除`flat`地形外其他地形可进行难度系数提升
+1. 支持legged_gym中的部分地形, 包括: `wave, slope, stairs up, stairs down, obstacles, flat`, 除`flat`地形外其他地形可进行难度系数提升
 2. 地面类型 (影响接触摩擦系数, 弹性摩擦系数), 包括: 橡胶地, 木地板, 瓷砖地
 
 ### 指标
@@ -41,8 +42,9 @@
 | 1 | `dof_limits` | 关节超出软关节范围的大小 | 软关节范围阈值 | 总关节变化范围 | `1-x` |
 | 2 | `lin_vel_err` | 线速度L2误差 | NA | 总线速度指令范围 | `1-x` |
 | 3 | `ang_vel_err` | 角速度L2误差 | NA | 总角速度指令范围 | `1-x` |
-| 4 | `base_height_std` | base高度变化方差 | NA | NA | `1-x` |
+| 4 | `dof_power` | 电机耗能 | 缩放系数 | 100 | `1-x` |
-| 5 | `dof_power` | 电机耗能 | NA | 10 | `1-x` |
+| 5 | `orientation_stability` | 机身姿态稳定性 (Roll) | NA | NA | `1-x` |
+| 6 | `torque_smoothness` | 力矩平滑度 | 缩放系数 | 30 | `1-x` |
 
 ### 速度追踪目标
 针对在虚实迁移中发现的问题, 整理指标 (metrics) 内容如下:
@@ -58,6 +60,7 @@
 总结速度最总目标 (goals) 如下:
 
 | # | 目标名称 Goals | 描述 | reset条件 | 最大reset次数 |
+| - | - | - | - | - |
 | 1 | `max_velocity` | 单一维度的最大线/角速度 | 每次执行一个方向的指令, 再急停 | 6 |
 | 2 | `diagonal_velocity` | 对角线速度变化 | 每次执行一对对角指令 | 8 |
 

UPDATE.md

@@ -1,5 +1,12 @@
 # UPDATE
+TODO: 在模型崩溃时也记录下最后的gauge信息
 ## 20251218
+### v0.1.10
+1. 加入`--write-tensorboard`参数, 默认为`False`即不记录`gauge`的日志信息
+2. 指令系数改为1.8 (比2.0稳定点)
+3. 完成全部指标, 新增`dof_power, orientation_stability, torque_smoothness`
+4. 加入雷达图绘图
+Fix Bugs: 日志记录重复的问题
 ### v0.1.9
 1. 在`MaxVelocityGoal`基础上加入`end_stance`, 最终保持站立姿态
 2. 在开始goal控制前, 先等机器人落地, 通过线速度小于0.05阈值判断静止后, 执行goal

robogauge/scripts/run_eval_models.sh

@@ -15,7 +15,7 @@ MODELS_FILE="$SCRIPT_DIR/evaluate_models.txt"
 RUN_PY="$SCRIPT_DIR/run.py"
 
 ### Default Configure ###
-EXP_NAME="go2_moe_flat"     # Experiment name [-n]
+EXP_NAME=""     # Experiment name [-n]
 TASK_NAME="go2_moe_flat"    # Task name [-t]
 SAVE_VIDEO=false            # Whether to save video [-s]
 
@@ -53,12 +53,16 @@ if [ ${#models_paths[@]} -eq 0 ]; then
 fi
 
 ### Run Evaluation Scripts ###
-base_args="--task $TASK_NAME --headless --experiment-name $EXP_NAME"
+base_args="--task $TASK_NAME --headless"
 
 if [ "$SAVE_VIDEO" = true ]; then
     base_args="$base_args --save-video"
 fi
 
+if [ -n "$EXP_NAME" ]; then
+    base_args="$base_args --exp-name $EXP_NAME"
+fi
+
 echo "================ Run Settings ================"
 echo "Script Dir: $SCRIPT_DIR"
 echo "Runner:     $RUN_PY"

robogauge/tasks/custom/go2_flat_task.py

@@ -1,3 +1,12 @@
+# -*- coding: utf-8 -*-
+'''
+@File    : go2_flat_task.py
+@Time    : 2025/12/18 20:19:25
+@Author  : wty-yy
+@Version : 1.0
+@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
@@ -22,15 +31,15 @@ class Go2FlatGaugeConfig(FlatGaugeConfig):
 
 class Go2FlatConfig(Go2Config):
     class commands(Go2Config.commands):
-        lin_vel_x = [-2.0, 2.0]  # min max [m/s]
+        lin_vel_x = [-1.8, 1.8]  # min max [m/s]
-        lin_vel_y = [-2.0, 2.0]  # min max [m/s]
+        lin_vel_y = [-1.8, 1.8]  # min max [m/s]
-        ang_vel_yaw = [-2.0, 2.0]  # min max [rad/s]
+        ang_vel_yaw = [-1.8, 1.8]  # min max [rad/s]
 
 class Go2MoEFlatConfig(Go2MoEConfig):
     class commands(Go2Config.commands):
-        lin_vel_x = [-2.0, 2.0]  # min max [m/s]
+        lin_vel_x = [-1.8, 1.8]  # min max [m/s]
-        lin_vel_y = [-2.0, 2.0]  # min max [m/s]
+        lin_vel_y = [-1.8, 1.8]  # min max [m/s]
-        ang_vel_yaw = [-2.0, 2.0]  # min max [rad/s]
+        ang_vel_yaw = [-1.8, 1.8]  # min max [rad/s]
 
     class control(Go2Config.control):
         # model_path = "{ROBOGAUGE_ROOT_DIR}/resources/models/go2/go2_moe_cts_124k.pt"

robogauge/tasks/gauge/base_gauge.py

@@ -26,7 +26,10 @@ from robogauge.tasks.gauge.goals import BaseGoal, MaxVelocityGoal, DiagonalVeloc
 from robogauge.tasks.gauge.metrics import *
 
 class BaseGauge:
-    def __init__(self, cfg: BaseGaugeConfig, robot_cfg: RobotConfig):
+    def __init__(self,
+            cfg: BaseGaugeConfig,
+            robot_cfg: RobotConfig,
+        ):
         self.cfg = cfg
         self.robot_cfg = robot_cfg
         self.goals_cfg = class_to_dict(self.cfg.goals)
@@ -79,7 +82,8 @@ class BaseGauge:
 
     def create_new_goal_logger(self):
         """ Create a new logger for new goal to metrics. """
-        if self.goal_idx >= len(self.goals): return
+        if self.goal_idx >= len(self.goals) or not self.cfg.write_tensorboard:
+            return
         logger.create_tensorboard(
             self.robot_cfg.robot_name,
             Path(self.robot_cfg.control.model_path).stem,

robogauge/tasks/gauge/base_gauge_config.py

@@ -11,6 +11,7 @@ from robogauge.utils.config import Config
 
 class BaseGaugeConfig(Config):
     gauge_class = 'BaseGauge'
+    write_tensorboard = False  # Whether to write tensorboard logs
 
     class assets:
         terrain_xml = '{ROBOGAUGE_ROOT_DIR}/resources/terrains/flat.xml'
@@ -34,7 +35,7 @@ class BaseGaugeConfig(Config):
         
         class visualization:
             enabled = True
-            dof_force = True
+            dof_torque = True
             dof_pos = True
         
         class lin_vel_err:
@@ -42,3 +43,14 @@ class BaseGaugeConfig(Config):
         
         class ang_vel_err:
             enabled = True
+        
+        class dof_power:
+            enabled = True
+            scaling_factor = 100.0  # [W] scaling factor for power metric
+        
+        class orientation_stability:
+            enabled = True
+        
+        class torque_smoothness:
+            enabled = True
+            scaling_factor = 30.0  # [Nm] scaling factor for torque smoothness metric

robogauge/tasks/gauge/gauge_configs/flat_gauge_config.py

@@ -36,7 +36,7 @@ class FlatGaugeConfig(BaseGaugeConfig):
         
         class visualization:
             enabled = True
-            dof_force = True
+            dof_torque = True
             dof_pos = True
 
         class lin_vel_err:

robogauge/tasks/gauge/metrics/__init__.py

@@ -1,4 +1,5 @@
 from .base_metric import BaseMetric
-from .dof_metrics import DofLimitsMetric
+from .dof_metrics import DofLimitsMetric, DofPowerMetric
 from .visualization import VisualizationMetric
 from .vel_metrics import LinVelErrMetric, AngVelErrMetric
+from .stable_metric import OrientationStabilityMetric, TorqueSmoothnessMetric

robogauge/tasks/gauge/metrics/base_metric.py

@@ -1,3 +1,12 @@
+# -*- coding: utf-8 -*-
+'''
+@File    : base_metric.py
+@Time    : 2025/12/18 20:18:45
+@Author  : wty-yy
+@Version : 1.0
+@Blog    : https://wty-yy.github.io/
+@Desc    : Base Metric Implementation
+'''
 from robogauge.utils.logger import logger
 from robogauge.tasks.robots import RobotConfig
 from robogauge.tasks.simulator.sim_data import SimData
@@ -10,6 +19,9 @@ class BaseMetric:
     def __init__(self, robot_cfg: RobotConfig, **kwargs):
         self.robot_cfg = robot_cfg
     
+    def reset(self):
+        pass
+
     def __call__(self, sim_data: SimData, goal_data: GoalData) -> float:
         value = 0.0
         logger.log(value, self.name, step=sim_data.n_step)

robogauge/tasks/gauge/metrics/dof_metrics.py

@@ -1,3 +1,12 @@
+# -*- coding: utf-8 -*-
+'''
+@File    : dof_metrics.py
+@Time    : 2025/12/18 20:18:24
+@Author  : wty-yy
+@Version : 1.0
+@Blog    : https://wty-yy.github.io/
+@Desc    : DOF Limits Metric Implementation
+'''
 import numpy as np
 
 from robogauge.tasks.robots import RobotConfig
@@ -47,3 +56,29 @@ class DofLimitsMetric(BaseMetric):
         rms_value = 1 - np.sqrt(np.mean(np.square(values)))
         logger.log(1 - rms_value, f'dof_limits/rms', step=sim_data.n_step)
         return rms_value
+
+class DofPowerMetric(BaseMetric):
+    """ Metric to log DOF power efficiency. """
+    name = 'dof_power_metric'
+
+    def __init__(self,
+        robot_cfg: RobotConfig,
+        scaling_factor: float = 100.0,
+        **kwargs
+    ):
+        super().__init__(robot_cfg)
+        self.scaling_factor = scaling_factor
+    
+    def __call__(self, sim_data: SimData, goal_data: GoalData) -> float:
+        values = []
+        for i in range(len(sim_data.proprio.joint.torque)):
+            torque = sim_data.proprio.joint.torque[i]
+            velocity = sim_data.proprio.joint.vel[i]
+            power = abs(torque * velocity)
+            values.append(power)
+            dof_name = sim_data.proprio.joint.names[i]
+            logger.log(power, f'dof_power/{dof_name}', step=sim_data.n_step)
+        rms_power = np.sqrt(np.mean(np.square(values)))
+        metric_power = 1 - rms_power / self.scaling_factor
+        logger.log(rms_power, f'dof_power/rms', step=sim_data.n_step)
+        return metric_power

robogauge/tasks/gauge/metrics/stable_metric.py

@@ -0,0 +1,61 @@
+# -*- coding: utf-8 -*-
+'''
+@File    : height_metric.py
+@Time    : 2025/12/18 20:18:33
+@Author  : wty-yy
+@Version : 1.0
+@Blog    : https://wty-yy.github.io/
+@Desc    : Orientation Stability, Torque Smoothness Metric Implementation
+'''
+import numpy as np
+
+from robogauge.tasks.robots import RobotConfig
+from robogauge.tasks.gauge.metrics.base_metric import BaseMetric, GoalData, SimData
+from robogauge.utils.math_utils import get_projected_gravity
+
+from robogauge.utils.logger import logger
+
+
+class OrientationStabilityMetric(BaseMetric):
+    """ Metric to log height stability. """
+    name = 'height_std_metric'
+
+    def __init__(self,
+        robot_cfg: RobotConfig,
+        **kwargs
+    ):
+        super().__init__(robot_cfg)
+    
+    def __call__(self, sim_data: SimData, goal_data: GoalData) -> float:
+        projected_gravity = get_projected_gravity(sim_data.proprio.base.quat)
+        projected_x = projected_gravity[0]
+        metric_value = 1 - abs(projected_x)  # consider roll only
+        logger.log(abs(projected_x), f'stable_metric/projected_x_abs', step=sim_data.n_step)
+        return metric_value
+
+class TorqueSmoothnessMetric(BaseMetric):
+    """ Metric to log torque smoothness. """
+    name = 'torque_smoothness_metric'
+
+    def __init__(self,
+        robot_cfg: RobotConfig,
+        scaling_factor: float = 30.0,
+        **kwargs
+    ):
+        super().__init__(robot_cfg)
+        self.last_torque = None
+        self.scaling_factor = scaling_factor
+    
+    def reset(self):
+        self.last_torque = None
+    
+    def __call__(self, sim_data: SimData, goal_data: GoalData) -> float:
+        current_torque = np.array(sim_data.proprio.joint.torque, np.float32)
+        if self.last_torque is None:
+            self.last_torque = current_torque
+            return 1.0  # No change at first step
+        torque_diff = current_torque - self.last_torque
+        rms_value = np.sqrt(np.mean(np.square(torque_diff)))
+        metric_value = 1.0 - rms_value / self.scaling_factor
+        logger.log(rms_value, f'stable_metric/torque_rms_diff', step=sim_data.n_step)
+        return metric_value

robogauge/tasks/gauge/metrics/vel_metrics.py

@@ -1,3 +1,12 @@
+# -*- coding: utf-8 -*-
+'''
+@File    : vel_metrics.py
+@Time    : 2025/12/18 20:18:56
+@Author  : wty-yy
+@Version : 1.0
+@Blog    : https://wty-yy.github.io/
+@Desc    : Velocity Metrics Implementation
+'''
 import numpy as np
 
 from robogauge.tasks.robots import RobotConfig

robogauge/tasks/gauge/metrics/visualization.py

@@ -1,3 +1,12 @@
+# -*- coding: utf-8 -*-
+'''
+@File    : visualization.py
+@Time    : 2025/12/18 20:19:09
+@Author  : wty-yy
+@Version : 1.0
+@Blog    : https://wty-yy.github.io/
+@Desc    : Visualization Metric Implementation
+'''
 from robogauge.tasks.robots import RobotConfig
 from robogauge.tasks.gauge.metrics.base_metric import BaseMetric, SimData, GoalData
 
@@ -9,20 +18,20 @@ class VisualizationMetric(BaseMetric):
 
     def __init__(self,
         robot_cfg: RobotConfig,
-        dof_force: bool = False,
+        dof_torque: bool = False,
         dof_pos: bool = False,
         **kwargs
     ):
         super().__init__(robot_cfg)
-        self.dof_force = dof_force
+        self.dof_torque = dof_torque
         self.dof_pos = dof_pos
 
     def __call__(self, sim_data: SimData, goal_data: GoalData) -> float:
-        for i in range(len(sim_data.proprio.joint.force)):
+        for i in range(len(sim_data.proprio.joint.torque)):
             name = sim_data.proprio.joint.names[i]
-            if self.dof_force:
+            if self.dof_torque:
-                force = sim_data.proprio.joint.force[i]
+                torque = sim_data.proprio.joint.torque[i]
-                logger.log(force, f'dof_force/{name}', step=sim_data.n_step)
+                logger.log(torque, f'dof_torque/{name}', step=sim_data.n_step)
             if self.dof_pos:
                 pos = sim_data.proprio.joint.pos[i]
                 logger.log(pos, f'dof_pos/{name}', step=sim_data.n_step)

robogauge/tasks/pipeline/base_pipeline.py

@@ -100,6 +100,9 @@ class BasePipeline:
                     first_reset = True
                     self.last_reset_time = sim_data.sim_time
                     sim_data = self.sim.step()
+        except Exception as e:
+            logger.error(f"❌ Pipeline execution failed with error: {e}")
+            raise e
         finally:
             self.sim.close_viewer()
             self.sim.close_video_writer()

robogauge/tasks/pipeline/multi_pipeline.py

@@ -144,7 +144,7 @@ class MultiPipeline:
         
         save_path = logger.log_dir / "aggregated_results.yaml"
         with open(save_path, 'w') as file:
-            yaml.dump(summary, file, allow_unicode=True)
+            yaml.dump(summary, file, allow_unicode=True, sort_keys=False)
         logger.info("✅ Aggregated execution finished.")
         logger.info(f"📁 Aggregated results saved to: {save_path}")
 

robogauge/tasks/robots/base_robot.py

@@ -45,19 +45,3 @@ class BaseRobot:
         action = np.zeros(self.num_action, dtype=np.float32)
         return action, self.p_gains, self.d_gains, self.control_type
 
-def get_projected_gravity(quat):
-    """ Compute world frame gravity (0, 0, -1) projected into robot base frame.
-    Args:
-        quat: (4,) quaternion (w, x, y, z) from robot base to world frame
-    Returns:
-        projected_gravity: (3,) projected gravity vector in robot base frame
-    """
-    qw, qx, qy, qz = quat
-
-    gravity_orientation = np.zeros(3)
-
-    gravity_orientation[0] = 2 * (-qz * qx + qw * qy)
-    gravity_orientation[1] = -2 * (qz * qy + qw * qx)
-    gravity_orientation[2] = 1 - 2 * (qw * qw + qz * qz)
-
-    return gravity_orientation

robogauge/tasks/robots/go2/go2.py

@@ -10,7 +10,8 @@
 import torch
 import numpy as np
 
-from robogauge.tasks.robots.base_robot import BaseRobot, get_projected_gravity
+from robogauge.tasks.robots.base_robot import BaseRobot
+from robogauge.utils.math_utils import get_projected_gravity
 from robogauge.tasks.robots.go2.go2_config import Go2Config
 from robogauge.tasks.simulator.sim_data import SimData
 from robogauge.tasks.gauge.goal_data import GoalData

robogauge/tasks/robots/go2/go2_moe.py

@@ -10,12 +10,7 @@
 import torch
 import numpy as np
 
-from robogauge.tasks.robots.base_robot import BaseRobot, get_projected_gravity
-from robogauge.tasks.robots.go2.go2_config import Go2Config
 from robogauge.tasks.robots.go2.go2 import Go2
-from robogauge.tasks.simulator.sim_data import SimData
-from robogauge.tasks.gauge.goal_data import GoalData
-from robogauge.utils.logger import logger
 
 class Go2MoE(Go2):
     def get_action(self, obs: np.ndarray):

robogauge/tasks/simulator/mujoco_simulator.py

@@ -200,7 +200,7 @@ class MujocoSimulator:
             joint=JointState(
                 pos=self.get_sensor_data('joint_pos'),
                 vel=self.get_sensor_data('joint_vel'),
-                force=self.get_sensor_data('joint_eff'),
+                torque=self.get_sensor_data('joint_eff'),
                 limits=self.dof_limits,
                 names=self.dof_names,
             ),
@@ -389,7 +389,7 @@ class MujocoSimulator:
         logger.info("Proprioception shapes:")
         logger.info(f"  joint.pos: { _shape(jp.pos) }")
         logger.info(f"  joint.vel: { _shape(jp.vel) }")
-        logger.info(f"  joint.force: { _shape(jp.force) }")
+        logger.info(f"  joint.torque: { _shape(jp.torque) }")
 
         logger.info(f"  base.pos: { _shape(bs.pos) }")
         logger.info(f"  base.quat: { _shape(bs.quat) }")

robogauge/tasks/simulator/sim_data.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass
 class JointState:
     pos: np.ndarray     # [rad] shape (n_dof,)
     vel: np.ndarray     # [rad/s] shape (n_dof,)
-    force: np.ndarray   # [N*m] shape (n_dof,)
+    torque: np.ndarray   # [N*m] shape (n_dof,)
     limits: np.ndarray  # [rad] shape (n_dof, 2), lower and upper limits
     names: list         # list of joint names
 

robogauge/utils/helpers.py

@@ -71,6 +71,8 @@ def parse_args():
         {"name": "--headless", "action": "store_true", "default": False, "help": "Run in headless mode."},
         {"name": "--save-video", "action": "store_true", "default": False, "help": "Save video output."},
         {"name": "--seed", "type": int, "default": 42, "help": "Random seed."},
+        {"name": "--write-tensorboard", "action": "store_true", "default": False, "help": "Write tensorboard logs."},
+        {"name": "--plot-radar", "action": "store_true", "default": False, "help": "Plot radar charts for metrics."},
 
         # Multiprocessing parameters, with different seeds
         {"name": "--multi", "action": "store_true", "default": False, "help": "Enable multiprocessing."},

robogauge/utils/logger.py

@@ -76,6 +76,13 @@ class Logger:
         self.logger = logging.getLogger(experiment_name + "_logger")
         self.logger.setLevel(log_level)
         self.logger.propagate = False
+
+        # Clear existing handlers to prevent duplicate logging
+        if self.logger.hasHandlers():
+            for handler in self.logger.handlers[:]:
+                handler.close()
+                self.logger.removeHandler(handler)
+
         self.time_tag = time.strftime("%Y%m%d-%H-%M-%S")
         self.tag = f"{self.time_tag}_{run_name}"
         self.experiment_name = experiment_name
@@ -141,8 +148,8 @@ class Logger:
         """
         if self.writer is not None:
             self.writer.add_scalar(tag, value, step)
-        else:
+        # else:
-            self.warning("Tensorboard writer is not initialized, skipping log.")
+        #     self.warning("Tensorboard writer is not initialized, skipping log.")
 
 logger = Logger()
 

robogauge/utils/math_utils.py

@@ -0,0 +1,18 @@
+import numpy as np
+
+def get_projected_gravity(quat):
+    """ Compute world frame gravity (0, 0, -1) projected into robot base frame.
+    Args:
+        quat: (4,) quaternion (w, x, y, z) from robot base to world frame
+    Returns:
+        projected_gravity: (3,) projected gravity vector in robot base frame
+    """
+    qw, qx, qy, qz = quat
+
+    gravity_orientation = np.zeros(3)
+
+    gravity_orientation[0] = 2 * (-qz * qx + qw * qy)
+    gravity_orientation[1] = -2 * (qz * qy + qw * qx)
+    gravity_orientation[2] = 1 - 2 * (qw * qw + qz * qz)
+
+    return gravity_orientation

robogauge/utils/radar_plot.py

@@ -0,0 +1,183 @@
+"""
+python robogauge/utils/radar_plot.py \
+    /home/xfy/Coding/robot_gauge/logs/go2_moe_flat_debug_multi/20251218-22-49-29_run_multi/aggregated_results.yaml \
+    /home/xfy/Coding/robot_gauge/logs/go2_flat_debug_multi/20251218-22-59-04_run_multi/aggregated_results.yaml \
+    --out logs/go2_flat_vs_moe_flat.png
+"""
+import matplotlib.pyplot as plt
+config = {
+    "font.family": 'serif', # 衬线字体
+    "figure.figsize": (6, 6),  # 图像大小
+    "font.size": 14, # 字号大小
+    "mathtext.fontset": 'cm', # 渲染数学公式字体
+    'axes.unicode_minus': False # 显示负号
+}
+plt.rcParams.update(config)
+
+import numpy as np
+import yaml
+import argparse
+import os
+import sys
+
+# 设置字体，尝试匹配参考图的衬线体风格 (如果系统没有会回退到默认)
+plt.rcParams['font.family'] = 'serif' 
+plt.rcParams['font.serif'] = ['Times New Roman', 'DejaVu Serif', 'serif']
+
+def parse_value_string(val_str):
+    if isinstance(val_str, (int, float)):
+        return float(val_str)
+    if isinstance(val_str, str):
+        if '±' in val_str:
+            return float(val_str.split('±')[0].strip())
+        return float(val_str)
+    return 0.0
+
+def load_data(file_paths):
+    all_data = []
+    
+    # 指标键名
+    metric_keys = [
+        'lin_vel_err', 
+        'ang_vel_err', 
+        'orientation_stability', 
+        'dof_limits', 
+        'torque_smoothness', 
+        'dof_power'
+    ]
+    
+    # 标签 (增加换行以避免拥挤)
+    labels_map = {
+        'lin_vel_err': 'Lin Vel\nAccuracy',
+        'ang_vel_err': 'Ang Vel\nAccuracy',
+        'dof_limits': 'Joint Limits\nMargin',
+        'dof_power': 'Energy\nEfficiency',
+        'orientation_stability': 'Orientation\nStability',
+        'torque_smoothness': 'Torque\nSmoothness',
+    }
+
+    for path in file_paths:
+        if not os.path.exists(path):
+            continue
+            
+        with open(path, 'r', encoding='utf-8') as f:
+            content = yaml.safe_load(f)
+            
+        raw_path = content.get('model_path', 'Unknown_Model')
+        # 简化图例名称：只取文件名，去掉 .pt
+        model_name = os.path.basename(raw_path).replace('.pt', '')
+        
+        # 如果名称过长，可以考虑进一步截断，例如：
+        # if len(model_name) > 20: model_name = model_name[:10] + "..." + model_name[-5:]
+
+        values = []
+        for k in metric_keys:
+            if k in content:
+                raw_val = content[k]['mean']
+                values.append(parse_value_string(raw_val))
+            else:
+                values.append(0.0)
+        
+        all_data.append({'name': model_name, 'values': values})
+        
+    return all_data, [labels_map[k] for k in metric_keys]
+
+def plot_radar(data_list, labels, output_file=None):
+    if not data_list:
+        print("No data to plot.")
+        return
+
+    num_vars = len(labels)
+    angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
+    angles += angles[:1] # 闭合
+    
+    # --- 颜色设置 ---
+    # 使用参考图类似的配色 (深蓝、浅蓝、绿等)
+    # 或者使用 'tab10', 'Set2' 等
+    colors = plt.cm.get_cmap("tab10", len(data_list))
+    
+    # 创建画布，稍微宽一点以便放图例
+    fig, ax = plt.subplots(figsize=(10, 8), subplot_kw=dict(polar=True))
+    
+    # --- 核心修改：调整布局 ---
+    # left=0.1, bottom=0.1, top=0.9 是为了给标题留空
+    # right=0.75 是关键！这意味着图表只占画布左边 75% 的宽度，右边 25% 留给图例
+    plt.subplots_adjust(left=0.05, right=0.75, top=0.9, bottom=0.1)
+
+    # 设置方向
+    ax.set_theta_offset(np.pi / 2)
+    ax.set_theta_direction(-1)
+    
+    # --- 绘制标签 ---
+    plt.xticks(angles[:-1], labels, color='#444444', size=13)
+    
+    # 标签对齐优化
+    for label, angle in zip(ax.get_xticklabels(), angles[:-1]):
+        if angle in (0, np.pi):
+            label.set_horizontalalignment('center')
+        elif 0 < angle < np.pi:
+            label.set_horizontalalignment('left')
+        else:
+            label.set_horizontalalignment('right')
+
+    # --- 绘制刻度 ---
+    ax.set_rlabel_position(0)
+    # 字体稍微调淡一点，不要抢眼
+    plt.yticks([0.25, 0.50, 0.75, 1.00], ["0.25", "0.50", "0.75", "1.00"], 
+               color="grey", size=10)
+    plt.ylim(0, 1.05)
+    
+    # 网格线：点状虚线，稍微粗一点
+    ax.grid(True, color='gray', linestyle=':', linewidth=1.5, alpha=0.5)
+    ax.spines['polar'].set_visible(False)
+
+    # --- 绘制数据 ---
+    # 加粗线条以匹配 bsuite 风格
+    linewidth = 3.0 
+    
+    for idx, item in enumerate(data_list):
+        values = item['values']
+        name = item['name']
+        values_closed = values + values[:1]
+        
+        color = colors(idx)
+        
+        ax.plot(angles, values_closed, linewidth=linewidth, linestyle='-', label=name, color=color)
+        ax.fill(angles, values_closed, color=color, alpha=0.2) # 填充透明度低一点
+
+    # --- 核心修改：图例位置 ---
+    # bbox_to_anchor=(1.1, 0.2) 的意思是：
+    # 锚点位于坐标轴右侧(1.1倍宽位置)，垂直方向在底部(0.2倍高位置)
+    # loc='upper left' 意思是图例的左上角对齐这个锚点
+    legend = plt.legend(
+        loc='upper left', 
+        bbox_to_anchor=(1.1, 0.3),  # 调整这里的 0.3 可以上下移动图例
+        title="Models", 
+        title_fontsize=16,
+        fontsize=12,
+        frameon=False, # 无边框
+        labelspacing=0.8 # 图例行间距
+    )
+    
+    # 设置图例标题对齐方式 (左对齐)
+    legend._legend_box.align = "left"
+
+    plt.title('Multi-Model Performance Comparison', size=18, y=1.08, color='#333333')
+    
+    if output_file:
+        plt.savefig(output_file, dpi=300, bbox_inches='tight') # bbox_inches='tight' 会自动裁剪白边
+        print(f"Plot saved to {output_file}")
+    else:
+        plt.show()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('files', metavar='F', type=str, nargs='+', help='YAML files')
+    parser.add_argument('--out', type=str, default=None, help='Output file')
+    
+    # 调试用（如果你直接运行脚本，请取消注释并填入你的文件名）
+    # sys.argv = ['plot.py', 'aggregated_results.yaml', 'aggregated_results2.yaml', '--out', 'fixed_radar.png']
+
+    args = parser.parse_args()
+    data, metrics_labels = load_data(args.files)
+    plot_radar(data, metrics_labels, output_file=args.out)

robogauge/utils/task_register.py

@@ -63,6 +63,8 @@ class TaskRegister():
             sim_cfg.viewer.headless = args.headless
         if args.save_video is not None:
             sim_cfg.render.save_video = args.save_video
+        if args.write_tensorboard is not None:
+            gauger_cfg.write_tensorboard = args.write_tensorboard
         if hasattr(args, 'friction') and args.friction is not None:
             sim_cfg.domain_rand.friction = args.friction
         if hasattr(args, 'base_mass') and args.base_mass is not None: