v1.0.1

robogauge/tasks/gauge/base_gauge.py

@@ -21,6 +21,7 @@ from robogauge.tasks.robots import RobotConfig
 from robogauge.tasks.gauge.base_gauge_config import BaseGaugeConfig
 from robogauge.tasks.gauge.goal_data import GoalData, VelocityGoal, PositionGoal
 from robogauge.tasks.simulator.sim_data import SimData
+from robogauge.tasks.gauge.gauge_configs.terrain_levels_config import SEARCH_LEVELS_TERRAINS
 
 from robogauge.tasks.gauge.goals import *
 from robogauge.tasks.gauge.metrics import *
@@ -149,7 +150,7 @@ class BaseGauge:
             for metric_name, quantiles in self.results[goal].items():
                 for quantile, val in quantiles.items():
                     metrics[metric_name][quantile].append(val)
-        self.results['summary'] = {}
+        self.results['summary'] = {'quality_score': {}, 'terrain_quality_score': {}}
         for metric_name, quantiles in metrics.items():
             if metric_name not in self.results['summary']:
                 self.results['summary'][metric_name] = {}
@@ -157,14 +158,19 @@ class BaseGauge:
                 mean = float(np.mean(vals))
                 std = float(np.std(vals))
                 self.results['summary'][metric_name][quantile] = f"{mean:.4f} ± {std:.4f}"
+                if metric_name == 'quality_score':
+                    tqs = mean
+                    if self.cfg.assets.terrain_name in SEARCH_LEVELS_TERRAINS:
+                        tqs = 0.09 * (self.cfg.assets.terrain_level - 1) + 0.19 * mean
+                    self.results['summary']['terrain_quality_score'][quantile] = f"{tqs:.4f} ± {std:.4f}"
 
         save_path = Path(logger.log_dir) / "results.yaml"
         self.results["terrain_name"] = self.cfg.assets.terrain_name
         self.results["terrain_level"] = self.cfg.assets.terrain_level
 
-        with open(save_path, 'w') as file:
-            yaml.dump(self.results, file, allow_unicode=True, sort_keys=False)
+        with open(save_path, 'w', encoding='utf-8') as file:
             yaml_str = yaml.dump(self.results, allow_unicode=True, sort_keys=False)
+            file.write(yaml_str)
         logger.info(
             f"""\n{'='*20} Goals and Metrics results {'='*20}\n"""
             f"""{yaml_str}"""

robogauge/tasks/gauge/base_gauge_config.py

@@ -9,6 +9,15 @@
 '''
 from robogauge.utils.config import Config
 
+QUALITY_WEIGHTS  = {  # Weights for geometric average, to calculate quality score
+    'lin_vel_err': 2,
+    'ang_vel_err': 2,
+    'dof_limits': 1,
+    'dof_power': 1,
+    'orientation_stability': 1,
+    'torque_smoothness': 1,
+}
+
 class BaseGaugeConfig(Config):
     gauge_class = 'BaseGauge'
     write_tensorboard = False  # Whether to write tensorboard logs

robogauge/tasks/gauge/goals/base_goal.py

@@ -12,6 +12,7 @@ from collections import defaultdict
 from robogauge.utils.measure import Average
 from robogauge.tasks.gauge.goal_data import GoalData
 from robogauge.tasks.simulator.sim_data import SimData
+from robogauge.tasks.gauge.base_gauge_config import QUALITY_WEIGHTS
 
 class BaseGoal:
     name = 'base_goal'
@@ -21,7 +22,8 @@ class BaseGoal:
         self.total = 0  # total tasks
         self.sub_name = None
 
-        self._goal_mean_metrics = defaultdict(list)
+        self.goal_metrics = defaultdict(list)
+        self.goal_quality_scores = []
 
     def pre_get_goal(self) -> bool:
         raise NotImplementedError
@@ -42,13 +44,19 @@ class BaseGoal:
     
     def update_metrics(self, metrics: dict):
         """ Update step metrics for the current goal."""
+        quality_score = 1.0
         for metric_name, value in metrics.items():
-            self._goal_mean_metrics[metric_name].append(value)
+            self.goal_metrics[metric_name].append(value)
+            quality_score *= min(max(1e-9, value), 1.0) ** QUALITY_WEIGHTS[metric_name]
+        quality_score = quality_score ** (1.0 / sum(QUALITY_WEIGHTS.values()))
+        self.goal_quality_scores.append(quality_score)
     
     @property
     def goal_mean_metrics(self):
         """ Get the mean metrics for the current goal. """
-        return {k: self._analysis_metrics(v) for k, v in self._goal_mean_metrics.items()}
+        result = {k: self._analysis_metrics(v) for k, v in self.goal_metrics.items()}
+        result['quality_score'] = self._analysis_metrics(self.goal_quality_scores)
+        return result
 
     @staticmethod
     def _analysis_metrics(metrics: list):