Robust Task-Based Design of Modular Manipulators for Single Joint Failure

Designing modular manipulators that can continue task execution despite joint failures remains a significant challenge in building reliable robotic systems. Due to the complexity of inverse kinematics calculations and the intractability of analyzing all failure conditions in continuous spaces, previous efforts have focused on constraining initial conditions or estimating generalized failure-tolerant regions in the workspace to limit the scope of the problem for analytical methods. In this work, we introduce a novel approach that distills the complex relationship between physical design and reachability into a neural network. This representation enables gradient-based optimization to efficiently generate designs tailored to specific task requirements, that are robust to arbitrary locked-joint failures. Simulation experiments demonstrate that our method produces manipulator designs with a higher task success rate under random failure conditions compared to baseline methods. Our code implementation can be found here.