Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization
arXiv:2510.09204v4 Announce Type: replace-cross Abstract: Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother trajectories, especially while planning in tight spaces. Unfortunately, it is often computationally intractable beyond a very small swarm size. In this paper, we propose Flow-Opt, a learning-based approach towards improving the computational tractability of centralized multi-robot trajectory optimization. Specifically, we reduce the problem to first learning a generative model to sample different
Advances in AI, particularly machine learning and differentiable optimization, are enabling solutions to computationally intractable problems in robotics that were previously out of reach.
This development addresses a critical barrier to deploying large-scale multi-robot systems, making complex coordinated tasks more feasible and efficient in various sectors.
The ability to efficiently optimize trajectories for large robot swarms will accelerate the development and adoption of autonomous multi-robot applications beyond small-scale deployments.
- · Robotics companies
- · Logistics and manufacturing sectors
- · AI algorithm developers
- · Defence contractors leveraging swarm robotics
- · Companies relying on manual labor for complex coordinated tasks
Multi-robot systems become more common and capable in industrial and service applications.
Increased demand for specialized hardware and software to support large-scale robot swarm operations.
New regulatory and ethical challenges emerge as autonomous robot swarms become more pervasive in public and private spheres.
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Read at arXiv cs.LG