![]() Finally, we conclude by exploring the fabrication and deployment of optimized soft robots. We then follow this up with an extension to allow for the optimization over discrete morphological parameters such as the number and configuration of limbs. ![]() Our approach efficiently optimizes both physical design and control parameters directly for task performance by leveraging a design-conditioned controller capable of generalizing over the space of physical designs. In our work, we start by proposing a data-efficient algorithm based on multi-task reinforcement learning. We then turn our attention to the related problem of task-driven optimization of robots and their controllers. In our work, we develop a deep learning approach to optimize both beacon placement and location inference directly for localization accuracy. Designing such a system involves placing beacons throughout the environment and inferring location from sensor readings. We start by considering the problem of optimizing a beacon-based localization system directly for localization accuracy. ![]() This thesis explores work on the task-driven co-optimization of robotics systems in an end-to-end manner, simultaneously optimizing the physical components of the system with inference or control algorithms directly for task performance. Therefore, it is often necessary to consider the interactions between these components when designing an embodied system. The ability of these systems to complete these tasks depends on a large range of technologies such as the mechanical and electrical parts that make up the physical body of the robot and its sensors, perception algorithms to perceive the environment, and planning and control algorithms to produce meaningful actions. Robots and intelligent systems that sense or interact with the world are increasingly being used to automate a wide array of tasks. ![]()
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