A Design Co-Pilot for Task-Tailored Manipulators

• URL: http://arxiv.org/abs/2509.13077v1
• Date: Tue, 16 Sep 2025 13:34:30 GMT
• Title: A Design Co-Pilot for Task-Tailored Manipulators
• Authors: Jonathan Külz, Sehoon Ha, Matthias Althoff,
• Abstract summary: This work proposes an approach to automatically designing and optimizing robot morphologies tailored to a specific environment.<n>A fully differentiable framework realizes gradient-based fine-tuning of designed robots and inverse kinematics solutions.<n> Numerical experiments show that our approach finds robots that can navigate cluttered environments, manipulators that perform well across a specified workspace, and can be adapted to different hardware constraints.
• Score: 21.513483509321805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although robotic manipulators are used in an ever-growing range of applications, robot manufacturers typically follow a ``one-fits-all'' philosophy, employing identical manipulators in various settings. This often leads to suboptimal performance, as general-purpose designs fail to exploit particularities of tasks. The development of custom, task-tailored robots is hindered by long, cost-intensive development cycles and the high cost of customized hardware. Recently, various computational design methods have been devised to overcome the bottleneck of human engineering. In addition, a surge of modular robots allows quick and economical adaptation to changing industrial settings. This work proposes an approach to automatically designing and optimizing robot morphologies tailored to a specific environment. To this end, we learn the inverse kinematics for a wide range of different manipulators. A fully differentiable framework realizes gradient-based fine-tuning of designed robots and inverse kinematics solutions. Our generative approach accelerates the generation of specialized designs from hours with optimization-based methods to seconds, serving as a design co-pilot that enables instant adaptation and effective human-AI collaboration. Numerical experiments show that our approach finds robots that can navigate cluttered environments, manipulators that perform well across a specified workspace, and can be adapted to different hardware constraints. Finally, we demonstrate the real-world applicability of our method by setting up a modular robot designed in simulation that successfully moves through an obstacle course.

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