Diffusion-Based Robot Control Policy with Safety Awareness and Generalization

Robotic systems operating in dynamic and unstructured environments require the integration of adaptability with rigorous safety guarantees. While diffusion models have recently emerged as powerful tools for learning complex, high-dimensional motion behaviors from demonstrations, they lack the formal assurances necessary for safety-critical applications. This research addresses this limitation by proposing an integrated framework that combines generative diffusion models with control-theoretic safety mechanisms and symbolic reasoning to generate robot motion that is not only diverse and adaptive but also verifiably safe and aligned with human expectations. A central contribution of this work is the embedding of safety constraints, derived from control theory and formal methods, directly into the generative process. The framework incorporates hard safety constraints using techniques such as passivity-based control, Control Barrier and Lyapunov Functions, enabling provably safe motion planning under dynamic and uncertain conditions. In addition, the system includes a reasoning-driven task monitoring module that continuously analyzes multimodal sensory inputs to anticipate potential failures. This module facilitates proactive behavioral adaptation and supports self-improvement over time. To ensure predictable and socially acceptable robot behavior, the framework also learns structured latent representations of human preferences. These representations guide motion synthesis in alignment with both perceived safety and comfort, alongside formal safety objectives. Real-time deployment is enabled through computational optimizations, including task-conditioned inference, policy distillation, and energy-aware execution strategies. Collectively, these contributions establish a principled framework for generating robotic motion that is safe, adaptive, and human-aligned, representing a step toward the development of safe and trustworthy artificial intelligence systems.