To enhance KINSCENE's articulation estimation capabilities for more complex articulated objects, several strategies can be employed. First, integrating advanced machine learning techniques, such as deep reinforcement learning or generative adversarial networks (GANs), could improve the model's ability to learn from diverse datasets that include a wider variety of articulated objects with multiple degrees of freedom. This would allow the system to generalize better to unseen objects and complex motions.
Second, incorporating multi-modal sensory data, such as combining RGB-D images with tactile feedback from the robot's gripper, could provide richer information about the object's physical properties and motion dynamics. This would enable KINSCENE to better infer articulation parameters, especially for objects that exhibit non-planar motion or intricate joint configurations.
Additionally, enhancing the articulation discovery stage with more sophisticated exploration strategies, such as adaptive sampling based on previous interaction outcomes, could lead to more efficient data collection and improved model accuracy. Implementing a hierarchical approach to articulation estimation, where simpler models are first constructed and then refined through iterative interactions, could also facilitate the handling of complex articulated structures.
Finally, leveraging simulation environments to pre-train the articulation estimation models on a variety of complex articulated objects could provide a robust foundation before deploying the system in real-world scenarios. This would help in addressing the challenges posed by occlusions and varying object geometries during physical interactions.