Scaffolding and Imitation Learning: Human Principles for Robots

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Acquiring Data for Learning

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

Robot Learning and Control

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

Perceptual Robotics: Integrating Vision and Action

Covers the integration of visual perception and robotic actions in embodied AI.

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.

Learning and Adaptive Control for Robots: Overview and Path Planning

Explores learning and adaptive control for robots, focusing on challenges, path planning with dynamical systems, and real-time planning applications.

Multimodal Analytics: Human-Robot Interaction and Learning Processes

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

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.

Learning Physically-Consistent Gaussian Mixture Model

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

Improving Robot Design: Data-Driven Approaches

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

Force Control with Dynamical Systems

Covers compliant and force control for robots using Dynamical Systems.