Improving Robot Design: Data-Driven Approaches

Related lectures (31)

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Explores learning and adaptive control for robots through SEDS and LPV-DS, emphasizing stability, non-linear dynamics, and optimization.

Explores learning and adaptive control for robots, emphasizing modulation of dynamical systems to improve stability and enable reactive motion.

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

Covers the basics of robot simulation and programming using Webots.

Delves into the necessity of complex organ systems in robots to enhance their abilities and lifespan.

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

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

Explores computational robot design, emphasizing co-optimization insights and the use of graph grammars for efficient design.

Discusses how robots manipulate objects using natural language instructions and integrates vision-language models for enhanced performance.

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