This paper introduces the optimal design manifold as a novel approach for understanding and optimizing the design of robotic systems. Existing optimization frameworks often jointly optimize design and behavior but lack insight into why specific designs are optimal for given tasks. Additionally, a functionally optimal design may not always be the most practical to build and practicality cannot always be captured by an objective function. By defining and learning the optimal design manifold, which represents the space of all optimal solutions, we provide a systematic method for exploring the design space and selecting the most practical optimal design. We apply the optimal design manifold to robot cell layout optimization, robot design optimization, and multi-camera placement and demonstrate its effectiveness in enhancing design choices by enabling a deeper understanding of what makes a design optimal.

While robotic assembly is a well researched topic, recycling and disassembly of products are also becoming ever more important as we transition to a more sustainable economy. In disassembly, we are typically only interested in a subset of product parts, which opens the possibility of using destructive processes such as tearing, cutting, or milling to speed up the disassembly. Currently, such destructive actions are only included as predefined case-specific actions such as milling away a screw head. By contrast, this paper presents a generalized approach to destructive disassembly planning that can automatically derive destructive disassembly actions from a symbolic representation of the disassembly state. Viable destructive actions are identified and verified only based on the underlying geometric model, circumventing the need for their explicit definition. We showcase the performance of this system both virtually on several test parts and physically by destructively and non-destructively disassembling a model of an electric motor using a robot manipulator with a multitool end effector.

The task-specific optimization of robotic systems has since the inception of the field been divided into the optimization of the robot and the optimization of the layout of its workstations. In this letter, we argue that these two problems are interdependent and should be treated as such. To this end, we present a unified problem formulation that enables for the simultaneous optimization of both the robot kinematics and the workstation layout. We demonstrate the effectiveness of our approach by jointly optimizing a robotic milling system. To compare our approach to the state of the art, we optimize the robot’s kinematics and layout separately. The results show that our approach outperforms the state of the art and that simultaneous optimization leads to up to eight times better solutions.