The STL motion-planning problem is reformulated as a shortest-path problem over a graph of convex sets to generate smooth Bézier-spline trajectories satisfying logical, timing, smoothness, and velocity constraints.
Faster algorithms for growing collision-free convex polytopes in robot configuration space
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
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cs.RO 5representative citing papers
X-Safe masks actions in configuration space using forward kinematics and quasi-static object models to give probabilistic collision-avoidance guarantees that transfer across robot embodiments without per-setup engineering.
ILD learns an invertible latent decomposition into unions of convex polytopes to support feasible path planning in high-DoF configuration spaces.
Neural CDF barriers enable efficient planning and distributionally robust safe control for manipulators in cluttered dynamic environments using only point-cloud observations.
Integrates CBBA task allocation with GCS trajectory optimization in time-extended space for multi-agent systems in dynamic cluttered environments.
citing papers explorer
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Signal Temporal Logic Motion Planning via Graphs of Convex Sets
The STL motion-planning problem is reformulated as a shortest-path problem over a graph of convex sets to generate smooth Bézier-spline trajectories satisfying logical, timing, smoothness, and velocity constraints.
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Any-Body Guard: Universal Safeguarding for Manipulation Policies via Action Masking
X-Safe masks actions in configuration space using forward kinematics and quasi-static object models to give probabilistic collision-avoidance guarantees that transfer across robot embodiments without per-setup engineering.
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Learning Unions of Convex Sets via Invertible Latent Decomposition for Path Planning
ILD learns an invertible latent decomposition into unions of convex polytopes to support feasible path planning in high-DoF configuration spaces.
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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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Task Allocation and Motion Planning in Dynamic, Cluttered Environments via CBBA and Graphs of Convex Sets
Integrates CBBA task allocation with GCS trajectory optimization in time-extended space for multi-agent systems in dynamic cluttered environments.