Yes, the insights gained from combining model-based analysis and learning-based control in legged locomotion can be effectively applied to other domains, including aerial and underwater robotics. The fundamental principles of reachability analysis, stability assessment, and control design are applicable across various robotic systems, regardless of their specific locomotion modalities.
In aerial robotics, for instance, the hybrid dynamics of drones can be analyzed using similar reachability frameworks. The ability to estimate RoAs for different flight maneuvers can enhance the stability of aerial vehicles during complex tasks, such as navigating through turbulent environments or performing acrobatic maneuvers. By employing deep learning techniques to approximate the HJ value functions, aerial robots can learn to adapt their flight strategies in real-time, improving their robustness to disturbances and enhancing overall performance.
In underwater robotics, the challenges of hydrodynamic forces and varying environmental conditions can also benefit from the proposed framework. The hybrid dynamics of underwater vehicles, which often involve both continuous motion and discrete events (such as buoyancy changes), can be modeled similarly to legged robots. The insights from RoA analysis can guide the design of control policies that ensure stable navigation and maneuvering in complex underwater environments.
Moreover, the integration of model-based and learning-based approaches can facilitate the development of adaptive control strategies that leverage both prior knowledge of the system dynamics and real-time learning from interactions with the environment. This hybrid approach can lead to more efficient and resilient robotic systems across various domains, ultimately advancing the field of robotics as a whole.