A new synthesis workflow converts finite-horizon minimum-time optimality into a cooling-demand VPC architecture and safety requirements into near-boundary tuning rules, matching nominal NMPC performance while showing zero temperature-limit violations under mismatch and faults where the NMPC benchmar
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CasADi – A software framework for nonlinear optimization and optimal con- trol
15 Pith papers cite this work. Polarity classification is still indexing.
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CAIP learns action-aligned visual representations via contrastive pre-training on human hand keypoints from egocentric video, outperforming DINOv2, SigLIP, MVP, and R3M with >30% gains on real dexterous manipulation tasks.
T-Rex introduces a large tactile dataset and MoT architecture that achieves over 30% higher success rates than baselines on 12 tasks requiring force control and deformable object handling.
DiffSlack introduces learnable slack variables and a damped Gauss-Newton projection to create a differentiable layer that enforces hard nonlinear inequality constraints in neural network outputs.
VBT-MPC performs robotic contour following by running MPC directly in vision-based tactile contour feature space and is tested on varied geometries in simulation and real experiments.
OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.
OLAhGP optimizes receding-horizon waypoints using the Gaussian process posterior over multiple steps to produce more informative paths for environmental monitoring than prior methods.
A vectorized reformulation of global self-optimizing control makes structural causality constraints linear for batch processes and enables a shortcut method that yields simple, repetitive combination matrices for near-optimal control, shown on a fed-batch reactor.
An iterative GP-based NMPC learning scheme for batch processes achieves 83% tracking error reduction after 4 iterations and 17-fold product mass increase by iteration 8 in simulations, matching full-model NMPC performance.
A filter line search SQP algorithm reduces iterations and computation time for nonconvex SOS programs compared to prior methods.
Neural CDF barriers enable efficient planning and distributionally robust safe control for manipulators in cluttered dynamic environments using only point-cloud observations.
LSTM-based neural predictions accelerate centralized optimization for aerial-ground handover trajectories, reporting over 3x speedup and 100% success rate versus cold starts.
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
A differentiable framework integrates function encoder-based neural ODEs with predictive control to enable zero-shot adaptation of explicit policies across families of nonlinear systems.
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Iterative Model-Learning Scheme via Gaussian Processes for Nonlinear Model Predictive Control of (Semi-)Batch Processes
An iterative GP-based NMPC learning scheme for batch processes achieves 83% tracking error reduction after 4 iterations and 17-fold product mass increase by iteration 8 in simulations, matching full-model NMPC performance.