Apply code simplification improvements

Simplifications made by code-simplifier agent:

1. sensor_feedback.py:
   - Added missing module-level logger definition
   - Fixes logger.warning() usage at line 291

2. robot_controller.py:
   - Removed unused variable kp = 100.0 in set_joint_velocities()
   - Updated comment from "PD controller" to "P controller on velocity"
   - More accurately reflects the actual implementation

3. multi_robot_coordinator.py:
   - Fixed status comparison to use RobotStatus.IDLE enum
   - Previously used string literals ["idle", "ready"]
   - Fixed return type annotation: str | None → TaskStatus | None

4. rl_integration.py:
   - Simplified _create_reward_function() control flow
   - Removed unreachable else clause
   - All TaskType enum values are explicitly handled

All changes preserve functionality while improving:
- Type safety (enum usage)
- Code clarity (removed dead code)
- Bug fixes (missing logger)
- Accuracy (correct type annotations and comments)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

Author: robotlearning123

src/mujoco_mcp/multi_robot_coordinator.py

@@ -374,7 +374,7 @@ def _process_tasks(self):
             available_robots = [
                 robot_id
                 for robot_id, state in self.robot_states.items()
-                if state.status in ["idle", "ready"]
+                if state.status == RobotStatus.IDLE
             ]
 
             # Allocate new tasks
@@ -548,7 +548,7 @@ def formation_control(
         self.task_allocator.add_task(task)
         return task.task_id
 
-    def get_task_status(self, task_id: str) -> str | None:
+    def get_task_status(self, task_id: str) -> TaskStatus | None:
         """Get status of a task"""
         with self.task_lock:
             if task_id in self.task_allocator.active_tasks:

src/mujoco_mcp/rl_integration.py

@@ -321,13 +321,10 @@ def _create_reward_function(self) -> TaskReward:
         if self.config.task_type == TaskType.REACHING:
             target = np.array([0.5, 0.0, 0.5])  # Default target position
             return ReachingTaskReward(target)
-        elif self.config.task_type == TaskType.BALANCING:
+        if self.config.task_type == TaskType.BALANCING:
             return BalancingTaskReward()
-        elif self.config.task_type == TaskType.WALKING:
-            return WalkingTaskReward()
-        else:
-            # Default reward function
-            return ReachingTaskReward(np.array([0.0, 0.0, 1.0]))
+        # TaskType.WALKING
+        return WalkingTaskReward()
 
     def _create_model_xml(self) -> str:
         """Create model XML for the RL task"""

src/mujoco_mcp/robot_controller.py

@@ -171,8 +171,7 @@ def set_joint_velocities(self, robot_id: str, velocities: List[float]) -> Dict[s
         controller["target_velocities"] = np.array(velocities)
         controller["control_mode"] = "velocity"
 
-        # Apply velocity control (simplified PD controller)
-        kp = 100.0  # Position gain
+        # Apply velocity control (simplified P controller on velocity)
         kv = 10.0  # Velocity gain
 
         for i in range(model.nu):

src/mujoco_mcp/sensor_feedback.py

@@ -14,6 +14,8 @@
 from abc import ABC, abstractmethod
 import logging
 
+logger = logging.getLogger(__name__)
+
 # Domain-specific types for type safety
 Quality = NewType("Quality", float)  # Sensor quality (0-1 range)
 Timestamp = NewType("Timestamp", float)  # Time in seconds since epoch