Vision-Language Models and Model Predictive Control: Recent Advances and Innovations

The integration of vision-language models (VLMs) and model predictive control (MPC) has emerged as a focal point for recent advancements in artificial intelligence and control systems. This report synthesizes the latest developments in these areas, highlighting the common themes of enhanced trustworthiness, granularity, and computational efficiency.

Vision-Language Models (VLMs)

Recent research in VLMs has centered on improving their reliability and adaptability, particularly in out-of-distribution detection (OoDD) and long-tail learning scenarios. Innovations such as self-guided prompting and image-adaptive concept generation have significantly bolstered the robustness of VLMs. Additionally, probabilistic approaches in pre-training have enhanced the models' understanding of image-text relationships, making them more adaptable to diverse scenarios.

Notable contributions include:

• Reflexive Guidance (ReGuide): Enhances OoDD capability through self-generated image-adaptive concepts.
• Denoise-I2W: A denoising approach for zero-shot composed image retrieval.
• YOLO-RD: Integrates a Retriever-Dictionary module into YOLO models for enhanced performance.
• Granularity Matters in Long-Tail Learning: Proposes methods to increase dataset granularity for better generalization.

Model Predictive Control (MPC)

Advancements in MPC are revolutionizing control systems, particularly in large-scale applications. Constraint-adaptive MPC frameworks are dynamically selecting subsets of constraints to reduce computational complexity while maintaining performance. These methods are crucial for systems with numerous state constraints, such as those in hyperthermia cancer treatments and metal additive manufacturing.

Key developments include:

• Constraint-adaptive MPC: Novel schemes dynamically select constraints for reduced computational complexity.
• Approximate Kalman filtering: Real-time feasible state estimation leveraging spatial correlations.
• Trajectory optimization for microstructure control: Uses augmented Lagrangian differential dynamic programming for precise material property control in metal AM.

These advancements collectively underscore the transformative potential of integrating VLMs and MPC in enhancing the efficiency, reliability, and adaptability of AI and control systems across various domains.