The explicit-implicit dual-state estimation approach presents several potential limitations and failure cases. One significant challenge is the reliance on accurate contact force estimation. If the contact encoder fails to accurately capture the forces acting on the robot's joints, it could lead to incorrect interpretations of the robot's state, resulting in inappropriate locomotion responses. This could be exacerbated in environments with highly variable surfaces or when the robot encounters unexpected obstacles.
Another limitation is the potential for overfitting to the training environment. If the training scenarios do not adequately represent the variability of real-world conditions, the robot may struggle to generalize its learned behaviors to new situations. This could lead to failure cases where the robot encounters obstacles it has not been trained on, resulting in collisions or falls.
To improve the dual-state estimation approach, several strategies could be employed. First, enhancing the robustness of the contact encoder through advanced sensor fusion techniques could provide more reliable state estimations. This could involve integrating additional proprioceptive sensors or using machine learning techniques to better interpret sensor data.
Second, implementing a more diverse training curriculum that includes a wider range of environmental conditions and obstacle types could help mitigate overfitting. By exposing the robot to varied scenarios during training, it would be better equipped to handle unexpected challenges in real-world applications.
Lastly, incorporating online learning capabilities could allow the robot to adapt its policy based on real-time feedback from its environment. This would enable continuous improvement of the dual-state estimation process, enhancing the robot's ability to navigate complex and dynamic environments effectively.