arXiv:2606.16564v1 Announce Type: cross Abstract: Robotic systems routinely encounter conflicting objectives, modeling errors, and degenerate contact conditions that render quadratic programs (QPs) infeasible. Yet most optimization solvers and differentiable QP layers assume feasibility, leading to numerical failures, unstable gradients, or solver breakdown when constraints cannot be simultaneously satisfied. We present Elastic ODYN, a primal--dual non-interior-point QP solver that handles infeasibility through smooth squared-$\ell_2$ elastic relaxations. The resulting formulation remains well
The development of more robust optimization techniques for robotics is crucial as AI advances rapidly, enabling robots to tackle more complex and unstructured environments.
This development addresses a fundamental challenge in robotics by improving the reliability and safety of AI-driven systems operating in real-world conditions, reducing computational failures.
Traditional QP solvers are prone to failure in complex robotic scenarios; Elastic ODYN offers a smoother, more stable solution by handling infeasibility, enabling more sophisticated robotic control and learning.
- · Robotics companies
- · AI developers
- · Automation sector
- · Companies relying on less robust QP solvers
- · Sectors reliant on manual labor in high-variability environments
More reliable and adaptable autonomous robotic systems in various applications.
Accelerated deployment of robots in unpredictable real-world settings, from logistics to hazardous material handling.
Increased public and industry trust in autonomous systems, leading to broader societal integration of advanced robotics.
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Read at arXiv cs.LG