arXiv:2606.13825v1 Announce Type: cross Abstract: Deep unfolding (DU) accelerates iterative optimizers by introducing learnable components and training them through unrolled iterations, but extending DU to the large-scale semidefinite programs (SDPs) common in robotics has remained limited. Unrolling a full-update conic solver such as COSMO exposes two obstacles that prior work on learned conic solvers has not: backpropagating through the per-iteration linear-system solve incurs memory quadratic in the problem size once the coefficient matrix is formed explicitly, and backpropagating through t
This research addresses a key limitation in applying deep unfolding techniques to large-scale optimization problems common in advanced robotics and AI development, signalling a new approach to improving their efficiency.
Improving the scalability of learned conic solvers directly contributes to the practical deployment of sophisticated AI in physically embodied systems, accelerating progress in areas like humanoid robotics and autonomous systems.
The ability to efficiently train deep unfolding algorithms for large-scale semidefinite programs removes a significant computational bottleneck, potentially enabling more complex and performant robotic control and planning.
- · AI algorithm developers
- · Robotics companies
- · Hardware manufacturers (for faster chips)
- · Defence tech sector
- · Traditional optimization software providers
More efficient and capable AI models for complex control tasks become feasible.
Faster development and deployment cycles for advanced robotic systems, including humanoid robots.
Increased automation capabilities in diverse sectors, potentially impacting labor markets and industrial processes.
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Read at arXiv cs.LG