---
license: mit
language:
- en
library_name: pytorch
tags:
- teleoperation
- autonomous-driving
- world-model
- video-prediction
- robotics
- carla
- pytorch
- predictive-display
- future-action-prediction
datasets:
- bimilab/TeleopWM-Dataset
---

# TeleopWM

**Aws Khalil, Jaerock Kwon**  
Bio-Inspired Machine Intelligence (BIMI) Lab  
University of Michigan–Dearborn

TeleopWM is a lightweight predictive latent world model for latency-resilient vision-based teleoperation. Given recent RGB observations and teleoperation control history, it predicts short-horizon future visual observations and future longitudinal/steering trends for predictive display.

TeleopWM is designed for short-horizon predictive display and future action forecasting under teleoperation latency while maintaining lightweight real-time inference characteristics.

## Paper and Project Links

- Project page: https://bimilab.github.io/paper-TeleopWM/
- GitHub repository: https://github.com/bimilab/paper-TeleopWM
- Paper: TBD
- Dataset: https://huggingface.co/datasets/bimilab/TeleopWM-Dataset
- YouTube demo: https://youtu.be/WeKqqZuwBl0

## Model Description

TeleopWM predicts 8 future RGB frames and future longitudinal/steering trends from recent visual observations and teleoperation control history. The model uses a SimVP visual backbone together with a TeleopWM latent dynamics branch, and is designed for real-time predictive display under teleoperation latency.

The checkpoint was trained and evaluated on CARLA/MILE-style driving rollouts. TeleopWM is intended as a compact research model for short-horizon predictive continuity, not as an open-ended video generation or autonomous-driving foundation model.

## Architecture

TeleopWM combines a SimVP visual backbone with a lightweight latent dynamics module and a motion-aware future action prediction head. The model jointly predicts future visual observations and future driving actions within a unified predictive framework designed for latency-resilient teleoperation.

![TeleopWM Method](TeleopWM-Method-v2.png)

## Intended Use

- Research on latency-resilient vision-based teleoperation
- Predictive display under communication latency
- Short-horizon future observation prediction
- Future action trend prediction
- CARLA/MILE-style driving rollout analysis

## Out-of-Scope Use

- Safety-critical autonomous driving deployment without validation
- Open-ended video generation
- Direct real-vehicle deployment without additional testing
- General-purpose world modeling outside the evaluated driving domain

## Files

- `best.pt` — final TeleopWM paper checkpoint
- `config.json` — training/evaluation configuration associated with the checkpoint
- `benchmark.json` — runtime benchmark summary
- `future_action_eval.png` — future action evaluation figure
- `main_rollout_action_figure_final.png` — qualitative rollout/action alignment figure

## Results Summary

| Category | Metric | Value |
|---|---:|---:|
| Rollout prediction | Horizon | 8 frames / approximately 533 ms at 15 FPS |
| Future action prediction | Outputs | longitudinal and steering trends |
| Runtime | Inference latency | 38.9 ms / rollout |
| Runtime | Prediction rate | 205.5 FPS |
| Runtime | Peak VRAM | 1.24 GB |
| Resolution | Input/output | 320x512 |

Runtime values are reference measurements from the final paper configuration and should be re-measured on target hardware.

## Qualitative Rollout Example

![TeleopWM qualitative rollout results](main_rollout_action_figure_final.png)

Representative 8-step future RGB rollouts and action alignment across straight, mild-turn, sharp-turn, and intersection scenarios.

## Future Action Prediction

![TeleopWM future action evaluation](future_action_eval.png)

Per-step future action error and correlation for longitudinal and steering predictions.

## Usage

Download the checkpoint and config:

```bash
huggingface-cli download bimilab/TeleopWM \
  best.pt config.json \
  --local-dir checkpoints/TeleopWM
```

Then evaluate using the TeleopWM repository:

```bash
python scripts/evaluate_teleopwm.py \
  --checkpoint checkpoints/TeleopWM/best.pt \
  --data-root /path/to/mile_action_diverse/test/Town05 \
  --split test \
  --sample-strategy uniform \
  --max-samples 64 \
  --device cuda
```

Runtime benchmarking:

```bash
python scripts/benchmark_teleopwm.py \
  --checkpoint checkpoints/TeleopWM/best.pt \
  --device cuda \
  --batch-size 1 \
  --warmup 20 \
  --iters 200
```

## Citation

If you use TeleopWM, please cite:

```bibtex
@misc{khalil2026teleopwm,
  title={TeleopWM: A Real-Time Predictive World Model for Latency-Resilient Vision-Based Teleoperation},
  author={Khalil, Aws and Kwon, Jaerock},
  year={2026},
  note={ResearchGate Preprint},
  doi={10.13140/RG.2.2.15259.84002}
}
```

## License

This model is released under the MIT License.