AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility (2409.15783v1)

Abstract: Recent works in the robot learning community have successfully introduced generalist models capable of controlling various robot embodiments across a wide range of tasks, such as navigation and locomotion. However, achieving agile control, which pushes the limits of robotic performance, still relies on specialist models that require extensive parameter tuning. To leverage generalist-model adaptability and flexibility while achieving specialist-level agility, we propose AnyCar, a transformer-based generalist dynamics model designed for agile control of various wheeled robots. To collect training data, we unify multiple simulators and leverage different physics backends to simulate vehicles with diverse sizes, scales, and physical properties across various terrains. With robust training and real-world fine-tuning, our model enables precise adaptation to different vehicles, even in the wild and under large state estimation errors. In real-world experiments, AnyCar shows both few-shot and zero-shot generalization across a wide range of vehicles and environments, where our model, combined with a sampling-based MPC, outperforms specialist models by up to 54%. These results represent a key step toward building a foundation model for agile wheeled robot control. We will also open-source our framework to support further research.

• The paper demonstrates a transformer-based universal dynamics model that enhances agile control across diverse wheeled robotic platforms using a two-stage training pipeline.
• It leverages massive synthetic and real-world data, employing techniques like input masking, noise addition, and adversarial attacks for robust model training.
• The study shows up to 54% improvement in positioning and velocity tracking, establishing a new benchmark for adaptive and agile robot control.

An Analysis of "AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility"

The paper "AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility" explores the development of a universal dynamics model tailored for enhancing performance and adaptability across a range of wheeled robotic platforms. The authors introduce a system dubbed AnyCar, a transformer-based model designed specifically to achieve specialist-like agility typically attained through extensive parameter tuning yet maintain the adaptability inherent in generalist models.

This paper is set against the backdrop of existing methodologies in robot learning, where recent advancements have paved the way for generalist control models applicable to various robotic architectures. However, these generalized models often fall short regarding the agility needed for high-performance navigation and control, especially in unpredictable environments. The proposed AnyCar framework aims to bridge this gap by leveraging the transformer architecture, proven successful in fields like perception and control, for robust and agile wheeled robotic control.

Methodology and Approach

1. Data Collection and Synthetic Generation: The authors establish a universal synthetic data generator capable of simulating a comprehensive array of vehicular dynamics across diverse terrains. Leveraging various simulators including MuJoCo, IsaacSim, and Assetto Corsa Gym, the paper highlights the generation of a substantial dataset (100 million data points) to underpin model training.
2. Robust Dynamics Model Training: AnyCar employs a two-stage training pipeline. The first phase involves pre-training with the massive simulated dataset, employing techniques like input masking, noise addition, and adversarial attacks to enhance model robustness. The second phase focuses on finetuning with real-world data, specifically collected to address simulation-to-reality discrepancies and state-estimation inaccuracies.
3. Adaptive Control Through MPPI: The paper utilizes Model Predictive Path Integral (MPPI) control, particularly a variant known as CoVO-MPC, which facilitates adaptive trajectory optimization using a learned dynamics model. This choice underscores the model's ability to provide precise, generalizable control outputs without extensive retraining for each unique robotic configuration.

Numerical Results and Evaluation

The research presents compelling results demonstrating AnyCar's capability in both few-shot and zero-shot scenarios, where it achieves superior performance compared to traditional specialist models. Notably, AnyCar shows up to 54% improvement in positioning and velocity tracking over baseline methods in various simulated and real-world settings. This clearly evidences the model's robustness and adaptability in the face of diverse and presumably challenging environments.

Implications and Future Developments

The implications of this research project into promising areas within the fields of robotics and artificial intelligence. By effectively combining the adaptability of generalist models with the refined control of specialized ones, AnyCar sets a precedent for future robotic systems capable of operating seamlessly across varying environmental conditions and tasks. This model provides a framework for a foundation model that could eventually encompass more complex real-world applications, such as fully autonomous vehicles or adaptable robotic systems capable of emergency response.

Future research could focus on further enhancing real-time computational efficiency, possibly through deeper integration with hardware accelerations, such as KV caching. Additionally, expanding the model to include visual navigation capabilities could provide a more holistic approach to machine autonomy in dynamic environments.

In conclusion, the paper delivers a thorough and technically robust advancement in robotics. By addressing rigid limitations present in both specialist and generalist models, AnyCar represents a significant stride toward building universally adaptive and agile vehicular control systems. This work lays the groundwork for future exploration and optimization in this emergent area of artificial intelligence and robotics.