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.
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CasADi: a software framework for nonlinear optimization and optimal control
11 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
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.
Physics-informed machine learning identifies a sparse control-affine model that is embedded in an adaptive tube MPC scheme for aerial vehicles, with stability proofs and demonstrated reductions in computation alongside improved tracking over baselines.
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.
citing papers explorer
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VBT-MPC: Vision-Based Tactile MPC for Contour Following
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.
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Generalized Global Self-Optimizing Control for Chemical Processes: Part II Objective-Guided Controlled Variable Learning Approach
OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.
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Multi-Step Gaussian Process Propagation for Adaptive Path Planning
OLAhGP optimizes receding-horizon waypoints using the Gaussian process posterior over multiple steps to produce more informative paths for environmental monitoring than prior methods.
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Physics-informed sparse identification-based tube model predictive control for aerial vehicles
Physics-informed machine learning identifies a sparse control-affine model that is embedded in an adaptive tube MPC scheme for aerial vehicles, with stability proofs and demonstrated reductions in computation alongside improved tracking over baselines.
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Global self-optimizing control of batch processes
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.
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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.
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On the Practical Implementation of a Sequential Quadratic Programming Algorithm for Nonconvex Sum-of-squares Problems
A filter line search SQP algorithm reduces iterations and computation time for nonconvex SOS programs compared to prior methods.
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Neural Configuration-Space Barriers for Manipulation Planning and Control
Neural CDF barriers enable efficient planning and distributionally robust safe control for manipulators in cluttered dynamic environments using only point-cloud observations.
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Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions
LSTM-based neural predictions accelerate centralized optimization for aerial-ground handover trajectories, reporting over 3x speedup and 100% success rate versus cold starts.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
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.
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Zero-Shot Function Encoder-Based Differentiable Predictive Control
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.