Intelligent Systems

Combining perception, learning, reasoning, and control to build systems that operate under uncertainty and act safely in the real world. Coverage includes model design and training, sensor fusion, state estimation, planning and decision-making, actuation, and human–machine interaction across robots, devices, and connected services. Applications span mobility, logistics, healthcare, manufacturing, and consumer products.

Defining features are an end-to-end loop—signal to state, state to decision, decision to action—with rigorous evaluation via simulation and field trials. Common elements include multimodal perception (vision, language, audio, proprioception), planning under constraints, edge–cloud orchestration for latency and scale, resilience via redundancy and fail-safes, and governance for transparency, bias, privacy, and security.

Effective practice yields architectures that match models and sensors to context, principled trade-offs among accuracy, latency, cost, and energy, calibrated use of people-in-the-loop, robust metrics for performance and safety, and pathways to harden prototypes into dependable deployments at scale.