RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation
Summary: arXiv:2604.19092v1 Announce Type: cross
Recent advances in large-scale video world models have significantly enhanced the capability for realistic future predictions. This progress paves the way for utilizing imagined videos in robot learning. Nonetheless, it is crucial to differentiate between visual realism and physical plausibility, as behaviors inferred from these generated videos may deviate from actual dynamics and lead to failures when implemented by embodied agents.
Current benchmarks have started to incorporate elements of physical plausibility; however, they predominantly focus on perception or diagnostic evaluations. A critical gap remains in systematically assessing whether predicted behaviors can be effectively transformed into executable actions that fulfill the intended tasks.
Introduction to RoboWM-Bench
To address this significant gap, we introduce RoboWM-Bench, a manipulation-centric benchmark designed for the embodiment-grounded evaluation of video world models. This innovative benchmark converts generated behaviors from both human-hand and robotic manipulation videos into actionable sequences intended for robotic execution.
Key Features of RoboWM-Bench
- Diverse Manipulation Scenarios: RoboWM-Bench encompasses a wide array of manipulation scenarios, providing a comprehensive platform for evaluation.
- Unified Evaluation Protocol: The benchmark establishes a standardized protocol for consistent and reproducible evaluations across different models and scenarios.
- Embodied Action Validation: The framework validates generated behaviors through direct robotic execution, ensuring that the predicted actions are not only plausible but also executable.
Evaluating Video World Models
Using RoboWM-Bench, we conducted evaluations on state-of-the-art video world models. Our findings indicate that the task of reliably generating physically executable behaviors continues to pose challenges. Several common failure modes were identified during our assessments, which include:
- Errors in Spatial Reasoning: Many models struggle with accurately predicting spatial relationships and object placements.
- Unstable Contact Prediction: The ability to predict stable contact points between objects remains inconsistent.
- Non-Physical Deformations: Some generated behaviors exhibit unrealistic physical properties that cannot be replicated in real-world applications.
Future Directions
While fine-tuning video world models using manipulation data has shown some improvements, physical inconsistencies persist. This points to significant opportunities for advancing more physically grounded video generation methods for robotic applications. The insights gained from RoboWM-Bench can guide future research in bridging the gap between visual and physical accuracy, ultimately enhancing the effectiveness of robotic manipulation.
In conclusion, RoboWM-Bench stands as a pioneering benchmark that not only addresses existing shortcomings in the evaluation of video world models but also sets the groundwork for future advancements in robotic manipulation capabilities.