A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI
This survey analyzes how physics simulators influence robotic navigation and manipulation, focusing on physical properties affecting sim-to-real transfer.
Key Findings
Methodology
This paper systematically evaluates various physics engines (e.g., MuJoCo, Bullet, PhysX) by analyzing their physical properties—such as collision response, friction models, and sensor noise—and their impact on training and transfer performance. Quantitative metrics like SRCC and Chamfer distance are used to compare how different parameter settings influence the predictive accuracy of simulation for real-world deployment. The study also assesses hardware requirements, providing recommendations for optimal configurations to enhance simulation fidelity and transfer reliability across tasks like goal-driven navigation and object manipulation.
Key Results
- Adjusting collision response models significantly improved transfer correlation (SRCC from 0.603 to 0.875), highlighting the importance of accurate collision physics.
- In manipulation tasks, high-fidelity friction models reduced physical errors by over 30%, improving grasp stability and realism.
- Randomization techniques, including visual and dynamics randomization, increased robustness, leading to more consistent real-world performance, especially in complex environments.
Significance
This work advances understanding of the relationship between simulator physics properties and real-world transfer success, providing a foundation for designing more predictive simulation platforms. By identifying key physical parameters that influence transferability, it addresses a critical bottleneck in deploying embodied AI systems in practical scenarios, such as autonomous navigation, industrial automation, and rescue robotics. The insights enable researchers to optimize simulation settings, reducing hardware costs and accelerating development cycles, thus bridging the gap between simulation and reality.
Technical Contribution
The paper offers a comprehensive comparison of multiple physics engines, establishing correlations between low-level physical parameters and transfer performance. It introduces a framework for systematic parameter tuning and proposes adaptive randomization strategies to mitigate simulation biases. The integration of quantitative metrics to evaluate simulation predictiveness marks a significant step toward standardized benchmarking, facilitating future research in simulation fidelity and transfer robustness. The work also explores the potential of learned world models, emphasizing the importance of physical consistency for predictive accuracy.
Novelty
Unlike prior surveys that focus mainly on high-level simulation features, this work emphasizes the impact of detailed physical modeling—such as contact mechanics, friction, and sensor noise—on transfer outcomes. It systematically correlates low-level physics parameters with real-world performance, providing a novel, data-driven approach to simulator calibration. This detailed analysis offers new insights into the core factors influencing sim-to-real success, filling a critical gap in current research.
Limitations
- The analysis primarily considers single-task scenarios, limiting insights into multi-task or multi-environment generalization.
- Parameter tuning relies heavily on extensive experimentation, which can be computationally expensive and time-consuming.
- Simulation of complex phenomena like fluid dynamics and deformable materials remains limited, restricting applicability in certain domains.
Future Work
Future research should focus on developing adaptive, learning-based methods for automatic parameter tuning, enabling simulators to self-optimize for diverse tasks. Expanding validation across multi-task and multi-environment settings will improve generalization. Additionally, integrating advanced physics models—such as fluid-structure interaction—will broaden applicability. Developing scalable, high-fidelity simulators that balance computational efficiency with physical accuracy remains a key goal to facilitate broader adoption in embodied AI applications.
AI Executive Summary
Robotic navigation and manipulation are fundamental capabilities driving advancements in Embodied AI, yet training directly in physical environments remains costly, risky, and time-consuming. Physics simulators have emerged as vital tools, enabling scalable data generation, safe testing, and rapid prototyping. However, the fidelity of these simulators—particularly their physical properties—directly influences the success of transferring learned policies to real robots. This survey critically examines leading physics engines like MuJoCo, Bullet, and PhysX, analyzing how their specific physical models—collision handling, friction, sensor noise—affect the predictive accuracy of simulation for real-world deployment.
The study highlights that precise modeling of contact dynamics, friction, and sensor imperfections significantly enhances transferability, with optimized parameters boosting correlation metrics (SRCC) from around 0.6 to over 0.87. Randomization strategies, such as visual and dynamics perturbations, further improve robustness, making policies more adaptable to real-world variability. The paper also compares hardware demands, offering practical recommendations for balancing simulation fidelity with computational resources.
Despite these advances, challenges remain. Simulating complex phenomena like fluid interactions or deformable objects at scale is still limited, constraining certain applications. The authors advocate for future work integrating adaptive, learning-based parameter tuning and multi-physics modeling, aiming to create more realistic and versatile simulators. Overall, this work provides a comprehensive framework for understanding and optimizing physics simulation for embodied robotic systems, paving the way for safer, more reliable deployment in industrial, service, and rescue scenarios.
Deep Dive
Abstract
Navigation and manipulation are core capabilities in Embodied AI, but training agents to perform them directly in the real world is costly, time-consuming, and unsafe. Therefore, sim-to-real transfer has emerged as a key approach, yet the sim-to-real gap persists. This survey examines how physics simulators address this gap by analyzing properties that have received limited attention in prior surveys. We also analyze their features for navigation and manipulation tasks, as well as their hardware requirements. Additionally, we offer a resource with benchmark datasets, metrics, simulation platforms, and methods to help researchers select suitable tools while accounting for hardware constraints.