refactor: merge motor sysid into unified sysid module

Unified the two separate sysid codepaths (motor-only and full-system)
into a single module that optimizes all 28 parameters jointly:

- 13 motor params (asymmetric gear, damping, friction, deadzone,
  Stribeck boost, action bias, filter tau, armature, ctrl_limit)
- 15 pendulum/arm params (mass, CoM, inertia, joint dynamics)

Key changes:
- Added stribeck_friction_boost, stribeck_vel, action_bias to
  ActuatorConfig (robot.py) and MJX runner
- Created shared src/sysid/preprocess.py (SG velocity recomputation)
- Rewrote src/sysid/rollout.py with unified MOTOR_PARAMS + PENDULUM_PARAMS
  spec and PARAM_SETS dict for flexible subset optimization
- Updated optimize.py, export.py, visualize.py to use unified params
  (removed all LOCKED_MOTOR_PARAMS references)
- Removed src/sysid/motor/ module and scripts/motor_sysid.py

Net: -1383 lines, zero code duplication between motor and full-system sysid.

src/sysid/visualize.py

@@ -35,7 +35,6 @@ def _run_sim(
     window_duration: float,
     sim_dt: float,
     substeps: int,
-    motor_params: dict[str, float],
 ) -> dict[str, np.ndarray]:
     """Run windowed or open-loop rollout depending on window_duration."""
     from src.sysid.rollout import rollout, windowed_rollout
@@ -44,11 +43,11 @@ def _run_sim(
         return windowed_rollout(
             robot_path=robot_path, params=params, recording=recording,
             window_duration=window_duration, sim_dt=sim_dt,
-            substeps=substeps, motor_params=motor_params,
+            substeps=substeps,
         )
     return rollout(
         robot_path=robot_path, params=params, actions=recording["action"],
-        substeps=substeps, motor_params=motor_params,
+        substeps=substeps,
     )
 
 
@@ -66,7 +65,6 @@ def visualize(
     import matplotlib.pyplot as plt
 
     from src.sysid.rollout import (
-        LOCKED_MOTOR_PARAMS,
         ROTARY_CARTPOLE_PARAMS,
         defaults_vector,
         params_to_dict,
@@ -75,12 +73,10 @@ def visualize(
     robot_path = Path(robot_path).resolve()
     recording = dict(np.load(recording_path))
 
-    motor_params = LOCKED_MOTOR_PARAMS
-
     sim_kwargs = dict(
         robot_path=robot_path, recording=recording,
         window_duration=window_duration, sim_dt=sim_dt,
-        substeps=substeps, motor_params=motor_params,
+        substeps=substeps,
     )
 
     t = recording["time"]
@@ -172,7 +168,6 @@ def visualize(
             sim_dt=sim_dt,
             substeps=substeps,
             window_duration=window_duration,
-            motor_params=motor_params,
         )
         title += f"\nOriginal cost: {orig_cost:.4f}  →  Tuned cost: {tuned_cost:.4f}"
         improvement = (1.0 - tuned_cost / orig_cost) * 100 if orig_cost > 0 else 0