Co-Designing Manipulation Systems Using Task-Relevant Constraints

A robotic system’s hardware and control policy must be co-optimized to ensure they complement each other to interact robustly with the environment. However, this com- bined search is extremely high-dimensional and intractable without a suitable underlying representation. This paper uses environmental constraints to structure the co-design space for manipulation. We show that task-relevant constraints encode regions of the search space containing reasonable co-design solutions. Furthermore, this underlying representation renders a co-design space amenable to gradient-based optimization. For efficient search, we present the co-design Jacobian that describes how the robot’s motion varies with control as well as hardware design changes. This Jacobian exploits the structure induced by environmental constraints for iterative design updates in the co-design space. Using these two conceptual tools, we co-design manipulators, grippers, and multi-fingered hands, showing that environmental constraints are an effective representation for co- designing diverse manipulation systems. Our methodology also scales well with increased co-design parameters, rendering the co-design of complex, high-dimensional manipulation systems feasible.