Feature Request: Support for OMX-AI in leLab

## Describe the Feature Request
Provide native support for the **OMX-AI** robotic arm in the `leLab` web GUI, alongside the current SO-100 series.

Currently, the underlying `lerobot` library officially supports Dynamixel motors and the OMX-AI platform. However, `leLab` restricts connection and usage to the hardcoded SO-101 leader/follower arms. Introducing abstraction for robot configurations will allow users of OMX-AI to also benefit from leLab's zero-code web UI for calibration, teleoperation, recording, and inference.

## Current Limitations / Code References
Here are the areas in the codebase that are currently hardcoded for SO-101:

### 1. Backend Configurations (`lelab`)
* **Teleoperation & Verification**: In `lelab/teleoperate.py` (lines 23–24), `SO101FollowerConfig`, `SO101Follower`, `SO101LeaderConfig`, and `SO101Leader` are directly imported. They are instantiated directly in lines 169–184.
* **Dataset Recording**: In `lelab/record.py` (lines 27–31), `SO101FollowerConfig` and `SO101LeaderConfig` are imported and instantiated on lines 159–166.
* **Calibration**: In `lelab/calibrate.py` (lines 256–263), calibration configuration setup specifically expects `SO101FollowerConfig` and `SO101LeaderConfig`.
* **Inference Rollouts**: In `lelab/rollout.py` (line 284), `--robot.type=so101_follower` is hardcoded as an argument for the rollout subprocess.

### 2. Frontend Calibration Configuration (`frontend`)
* **Joint Calibration Targets**: In `frontend/src/lib/calibrationTargets.ts` (lines 4–25), the target calibration limits are explicitly hardcoded for `SO101_LEADER_TARGETS` and `SO101_FOLLOWER_TARGETS` (e.g., `shoulder_pan`, `shoulder_lift`, `elbow_flex`, `wrist_flex`, `wrist_roll`, `gripper`).

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## Proposed Solutions
1. **Dynamic Robot Configuration Loading**:
* Refactor the backend (e.g., `teleoperate.py`, `record.py`, `calibrate.py`) to dynamically import and build configuration classes depending on the target robot specified (e.g., loading `OMXAIFollowerConfig` / `OMXAILeaderConfig` if `omx-ai` is chosen).
2. **Abstract Calibration Targets on the Frontend**:
* Expose the calibration joint targets dynamically from the backend (by reading properties from the respective `lerobot` robot class) or refactor the frontend configuration to support mappings based on the active robot configuration profile.
3. **Parameterize Rollouts**:
* Allow the frontend or user configuration file to dynamically set the `--robot.type` parameter instead of hardcoding it in `rollout.py`.

Start by tracing the SO-101-specific configuration paths in lelab/teleoperate.py, lelab/record.py, lelab/calibrate.py, and lelab/rollout.py, then inspect frontend/src/lib/calibrationTargets.ts. The work is complete when OMX-AI can be selected and supported across teleoperation, recording, calibration, inference rollouts, and frontend calibration targets.

Written by the indexing model from the issue text.