Improving Robot Design: Data-Driven Approaches

This lecture explores data-driven approaches to improve robot design, focusing on the embodied model of robots, compliance, under-actuation, soft materials, and complex interactions. It delves into challenges in robot design, such as compliance modeling and the high-dimensional search space. The lecture discusses utilizing and creating data for design, research goals in automating robot design, and novel fabrication tools. It also covers the design and optimization of an anthropomorphic soft skeleton hand, focusing on achieving complex passive behaviors. The lecture concludes with insights on learning from physical interactions, combining physical and learning responses, and the application of self-healing sensors in robotics.