Physical AI Safety

Safe physical intelligence

From Robust Learning to Stable Physical Intelligence

Physical AI systems operate through continuous interaction with the real world. They must understand human intent, perceive uncertain environments, predict future states, plan reliable actions, execute them through physical actuators, and continuously monitor whether their behavior remains safe.
Our research develops the learning algorithms and mathematical foundations required to make this closed-loop intelligence robust, stable, privacy-preserving, explainable, and verifiable. We focus particularly on reliable perception under realistic corruption, predictive and generative modeling, robust and certifiable decision intelligence, dynamical-system stability, global optimization, and runtime safety assurance.

What Makes Our Approach Distinctive

Many Physical AI studies focus on robot architectures, demonstrations, or task-specific performance. We approach Physical AI safety from both modern learning theory and mathematical systems analysis.

Robust and reliable perception algorithms for Physical AI in uncertain and adversarial environments

Temporal dynamics modeling for state prediction, realistic trajectory generation, and simulation based safety evaluation

Learning theoretic foundations for dependable planning and risk aware decisions

Mathematical foundations for safe physical behavior via nonlinear optimization and stability analysis

Runtime safety assurance, model explainability, and threat defense to enable safe system recovery and human oversight

System-wide trustworthy AI foundations for Physical AI: privacy, fairness, security, and representation stability

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