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Improving Robot Design: Data-Driven Approaches

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

Acquiring Data for Learning

Explores training robots through reinforcement learning and learning from demonstration, highlighting challenges in human-robot interaction and data collection.

Learning Physically-Consistent Gaussian Mixture Model

Explores Physically-Consistent Gaussian Mixture Models for robot control and trajectory learning.

Compliant Control for Robots: Impedance and Variable Stiffness

Explores compliant control for robots through impedance and variable stiffness, enabling safe and adaptive interactions with the environment.

Robots: Safe Collaboration

Covers challenges and solutions for robots to work safely with humans, emphasizing adaptability and predictability.