Pose graph optimization is recast as damped Riemannian dynamics on Lie groups, enabling a fully distributed algorithm with a semi-implicit integrator that converges under both synchronous and asynchronous communication.
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Automated Vehicle Highway Merging: Motion Planning via Adaptive Interactive Mixed-Integer MPC
13 Pith papers cite this work. Polarity classification is still indexing.
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A learned feedback policy replaces manual parameter tuning in distributed Riemannian optimization over matrix Lie groups, achieving lower objective values on multi-robot mapping benchmarks.
Differential halo zonotopes enable static verification of global robustness in DNNs by jointly propagating pairs of perturbed inputs while bounding divergence, with a relaxed confidence-based variant.
Internal attention heads in VLA policies localize targets for a CBF safety filter that enables real-time collision avoidance with dynamic obstacles and outperforms init-time oracle identification by 43% on average.
Koopman lifting enhances extremum seeking control to deliver faster and more robust oscillation damping on a forced time-varying Van der Pol oscillator than standard ESC on measured states.
A new matrix zonotope perturbation method with coefficient-space approximation enables faster and less conservative data-driven reachability analysis than prior CMZ or MZ approaches.
Integrates iterative learning control with a torque library to enable high-precision adaptive locomotion on bipedal and quadrupedal robots, reducing tracking errors by up to 85% and achieving over 30x faster control rates.
Presents a polyhedral enclosure algorithm encoded as MILP to produce sound over-approximations of forward reachable sets for nonlinear neural feedback systems.
AACC combines online IOC for driving style identification with a Stackelberg game planner to proactively protect right-of-way against cut-ins, reporting up to 79.8% safety gains in simulation.
Wake-aware stochastic scheduling for wind farms using FLORIS and wake steering yields 3-5% higher revenue than conventional power-curve methods in a London Array GB-market case study.
Optimization and simulation analyses identify an optimal battery capacity for Class 7-8 electric trucks due to cost-weight-range trade-offs and indicate fleet electrification can reach cost viability at low penetration levels with falling battery prices and cheap depot electricity.
An OFO algorithm for day-ahead pricing uses aggregate loads to achieve near-Stackelberg peak reduction with lower computation and no individual data.
citing papers explorer
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Distributed Pose Graph Optimization via Continuous Riemannian Dynamics
Pose graph optimization is recast as damped Riemannian dynamics on Lie groups, enabling a fully distributed algorithm with a semi-implicit integrator that converges under both synchronous and asynchronous communication.
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Learning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups
A learned feedback policy replaces manual parameter tuning in distributed Riemannian optimization over matrix Lie groups, achieving lower objective values on multi-robot mapping benchmarks.
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Differential Zonotopes for Verifying Global Robustness of DNNs
Differential halo zonotopes enable static verification of global robustness in DNNs by jointly propagating pairs of perturbed inputs while bounding divergence, with a relaxed confidence-based variant.
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Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models
Internal attention heads in VLA policies localize targets for a CBF safety filter that enables real-time collision avoidance with dynamic obstacles and outperforms init-time oracle identification by 43% on average.
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Data-Driven Koopman-Enhanced Extremum Seeking for Oscillation Damping in Nonlinear Systems
Koopman lifting enhances extremum seeking control to deliver faster and more robust oscillation damping on a forced time-varying Van der Pol oscillator than standard ESC on measured states.
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Data-Driven Reachability Analysis Using Matrix Perturbation Theory
A new matrix zonotope perturbation method with coefficient-space approximation enables faster and less conservative data-driven reachability analysis than prior CMZ or MZ approaches.
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Iteratively Learning Muscle Memory for Legged Robots to Master Adaptive and High Precision Locomotion
Integrates iterative learning control with a torque library to enable high-precision adaptive locomotion on bipedal and quadrupedal robots, reducing tracking errors by up to 85% and achieving over 30x faster control rates.
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Polyhedral Enclosures: An Efficient Combinatorial Abstraction for Nonlinear Neural Feedback Systems
Presents a polyhedral enclosure algorithm encoded as MILP to produce sound over-approximations of forward reachable sets for nonlinear neural feedback systems.
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Anti-bullying Adaptive Cruise Control: A proactive right-of-way protection approach
AACC combines online IOC for driving style identification with a Stackelberg game planner to proactively protect right-of-way against cut-ins, reporting up to 79.8% safety gains in simulation.
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Optimal wind farm energy and reserve scheduling incorporating wake interactions
Wake-aware stochastic scheduling for wind farms using FLORIS and wake steering yields 3-5% higher revenue than conventional power-curve methods in a London Array GB-market case study.
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Coupled Routing and Charge Schedule Optimization of Electrified Delivery Truck Fleets: Feasibility Analyses
Optimization and simulation analyses identify an optimal battery capacity for Class 7-8 electric trucks due to cost-weight-range trade-offs and indicate fleet electrification can reach cost viability at low penetration levels with falling battery prices and cheap depot electricity.
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Load Management of Distribution Systems via Online Dynamic Pricing
An OFO algorithm for day-ahead pricing uses aggregate loads to achieve near-Stackelberg peak reduction with lower computation and no individual data.
- Proximal Gradient Dynamics and Feedback Control for Equality-Constrained Composite Optimization