The field of robot control and navigation is moving towards more advanced and adaptive methods, with a focus on improving safety, efficiency, and comfort in dynamic environments. Researchers are exploring new approaches to model predictive control, such as incorporating time-varying model parameters and learning-based trajectory prediction, to better handle complex and uncertain scenarios. Additionally, there is a growing interest in developing end-to-end locomotion policies that can directly map sensory inputs to motor commands, enabling more robust and agile navigation in cluttered scenes. Another key area of research is the development of computationally efficient neural network controllers that can operate within strict hardware limitations, with a focus on model compression and stability guarantees. Notable papers in this area include:

• A novel approach to model predictive control that uses time-varying model parameters to anticipate previously unmodeled phenomena, demonstrating significant reductions in long-horizon prediction errors.
• A learning-based trajectory prediction method that improves safety and motion smoothness in crowded scenes, with real-world deployment revealing discrepancies between open-loop metrics and closed-loop performance.
• An end-to-end locomotion policy that directly maps raw LiDAR point clouds to motor commands, enabling robust navigation in dynamic scenes while formally separating safety from task objectives.
• A comprehensive model compression methodology that leverages component-aware structured pruning to determine the optimal pruning magnitude for each pruning group, ensuring a balance between compression and stability for neural network controller deployment.