Motor Intelligence for Soft Robots

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Computational Robot Design: Differentiable Simulation and Control

Explores optimization-driven design, differentiable simulation, control strategies, and the evolution of virtual creatures and soft machines.

Linear Quadratic Optimal Control: Theory and Applications

Explores Linear Quadratic optimal control theory, covering FH-LQ and IH-LQ problems and the importance of observability in control systems.

Safe Learning for Robotics

Covers safe learning for robotic systems, including safety assurances and control theory.

Safe Learning for Robotics

Explores safe and data-efficient visual learning for robotics, covering control theory, perception systems, end-to-end learning, and expert policy.

Controllability and Reachability

Explores reachability and controllability in multivariable control systems, discussing tests, proofs, and their implications.

Robotics: The State!

Explores the state of robotics research, covering interdisciplinary challenges, sensor technologies, and human-robot collaboration architectures.

Active Materials Dynamics: A Mathematical Framework

Delves into the theory of material activation, proposing a unified mathematical framework to model how multiple stimuli can produce changes at the macroscopic level.

Robot Learning and Control

Covers learning and adaptive control for robots, focusing on real-time reactivity and path planning using dynamical systems.

Robotics Drive Selection

Explores two paradigms for robotics, components like motors, gears, sensors, and controllers, and design considerations for various types of robots.

Acquiring Data for Learning: Modern Approaches and Challenges

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