KaRMA is a kinematic metric that quantifies reachable in-hand translation and reorientation of a spherical object in robotic hands via constrained rolling motions and reports translational coverage, rotational coverage, and sensitivity to initial grasp.
OSQP: An operator splitting solver for quadratic programs
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
NLPOpt-Net is an unsupervised neural architecture that learns parametric solutions to constrained NLPs by pairing a backbone network with quadratic projection layers that guarantee feasibility and near-zero constraint violations.
Learned online policies for the ADMM relaxation parameter improve iteration count and runtime on benchmark quadratic programs while maintaining convergence guarantees for time-varying parameters under mild assumptions.
Introduces Full-Covariance-Consensus, Partial-Covariance-Consensus, and Mean-Consensus distributed covariance steering methods via non-convex ADMM, with convergence guarantees for the latter two and demonstrations of scalability to thousands of agents.
citing papers explorer
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KaRMA: A Kinematic Metric for Fine Manipulation Ability in Robotic Hands
KaRMA is a kinematic metric that quantifies reachable in-hand translation and reorientation of a spherical object in robotic hands via constrained rolling motions and reports translational coverage, rotational coverage, and sensitivity to initial grasp.
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NLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility Guarantees
NLPOpt-Net is an unsupervised neural architecture that learns parametric solutions to constrained NLPs by pairing a backbone network with quadratic projection layers that guarantee feasibility and near-zero constraint violations.
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Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
Learned online policies for the ADMM relaxation parameter improve iteration count and runtime on benchmark quadratic programs while maintaining convergence guarantees for time-varying parameters under mild assumptions.
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Distributed Covariance Steering via Non-Convex ADMM for Large-Scale Multi-Agent Systems
Introduces Full-Covariance-Consensus, Partial-Covariance-Consensus, and Mean-Consensus distributed covariance steering methods via non-convex ADMM, with convergence guarantees for the latter two and demonstrations of scalability to thousands of agents.