A Design Co-Pilot for Task-Tailored Manipulators

2025-09-16

AI Summary

To address the contradiction between poor task adaptability of general-purpose manipulators and the lengthy, costly customization process, this paper proposes a task-driven robotic morphology auto-design method. Our approach constructs a fully differentiable inverse kinematics model and integrates generative neural networks with gradient-based optimization to enable end-to-end joint optimization of structural parameters and control policies. The framework reduces traditional design cycles—typically hours—to seconds, enabling real-time human-in-the-loop co-design and hardware-aware morphology generation. We validate the method through both simulation and physical experiments on a modular robot platform: the automatically generated manipulators satisfy specified workspaces, complex environmental navigation requirements, and diverse hardware constraints, and successfully complete obstacle-course trials on hardware. The core contribution is the first realization of integrated, task–structure–control co-design enabled by differentiable modeling.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Automates design of task-specific robot manipulators
• Overcomes high cost and slow custom robot development
• Enables rapid adaptation to specific environments and constraints

Innovation

• Differentiable framework for gradient-based robot fine-tuning
• Learning inverse kinematics across diverse manipulator morphologies
• Generative design accelerating specialized robot creation to seconds