Robot manipulation research is essential for advancing automation technologies, allowing robotic arms to execute complex and precise tasks across various industries. In recent years, several technologies have matured and transformed industries, such as logistics and manufacturing automation, through advancements like robot grasping. To enable the adoption of these emerging capabilities into real-world automation scenarios, these technologies need to be both efficient and reliable. However, achieving a balance between efficiency and reliability is challenging, as improving one often requires compromising the other. As a result, many impressive methods, initially developed for practical applications, struggle to make the transition into industry use.

This thesis explores five research areas within the field of robot manipulation, examining diverse angles and domains such as industrial automation, deformable manipulation, agricultural robotics, and surgical robotics. It proposes strategies to achieve efficient and reliable robot manipulation. By focusing on enhancing both efficiency and reliability, the thesis aims to facilitate the transition of robot manipulation from proof of concept to practical applications. Among the various methods explored, three primary strategies have proven to be particularly effective: constrained optimization, which given a reliable model, applies strict mathematical constraints to find efficient and reliable solutions; interactive perception and self-supervised learning, which are used to improve the efficiency and reliability in situations where there is high uncertainty in the dynamics of the system or a lack a reliable model. The thesis concludes by discussing the key insights gained through this research, reviewing lessons learned, and suggesting potential directions for future research.