Learning and Adaptive Control for Robots: SEDS & LPV-DS

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Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Explores data-driven approaches to improve robot design, focusing on compliance, soft materials, and complex interactions.

Delves into Human-Robot Interaction, student engagement, and learning analytics using multimodal data to enhance educational processes.

Introduces dynamical systems, equilibrium points, stability, vector fields, phase plots, and Lyapunov stability.

Explores learning, adaptive control, cutting soft tissue, and robotic implementation for robots.

Explores learning and control for complex systems, addressing challenges and opportunities in technology and interdisciplinary research.

Delves into the definition and capabilities of robots based on various sources.

Explores challenges and future directions of collaborative robots in real-world scenarios from a human-centric perspective.

Covers Lyapunov stability analysis, focusing on Lyapunov functions and stability criteria in robotic systems.