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.
Sampling-based algorithms for optimal motion planning
7 Pith papers cite this work. Polarity classification is still indexing.
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2026 7representative citing papers
Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.
A new alignment heuristic and star-shaped simplex chain construction for feedback motion planning reduces average path bending by 91.4% and LQR effort by 45.5% while remaining computationally efficient.
A vision-guided hybrid rigid-soft manipulator achieves consistent sub-2cm reaching in unseen cluttered environments via shape-aware planning and learning control without environment-specific retraining.
A hybrid navigation system uses offline HJ reachability computations as heuristics and safety constraints within graph search to achieve faster and safer robot movement in complex indoor environments.
A hybrid search-plus-optimal-control framework that produces optimized, kinematically feasible trajectories for multiple agents by warm-starting an OCP from an initial feasible solution.
A literature review of intelligent automation approaches using robotics, AI, and control for disassembly, inspection, sorting, and reprocessing of end-of-life electronics.
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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Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models
Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.
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Smooth Feedback Motion Planning with Reduced Curvature
A new alignment heuristic and star-shaped simplex chain construction for feedback motion planning reduces average path bending by 91.4% and LQR effort by 45.5% while remaining computationally efficient.
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HyReach: Vision-Guided Hybrid Manipulator Reaching in Unseen Cluttered Environments
A vision-guided hybrid rigid-soft manipulator achieves consistent sub-2cm reaching in unseen cluttered environments via shape-aware planning and learning control without environment-specific retraining.
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A Hamilton-Jacobi Reachability-Guided Search Framework for Efficient and Safe Indoor Planar Robot Navigation
A hybrid navigation system uses offline HJ reachability computations as heuristics and safety constraints within graph search to achieve faster and safer robot movement in complex indoor environments.
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Optimized and kinematically feasible multi-agent motion planning
A hybrid search-plus-optimal-control framework that produces optimized, kinematically feasible trajectories for multiple agents by warm-starting an OCP from an initial feasible solution.
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Redefining End-of-Life: Intelligent Automation for Electronics Remanufacturing Systems
A literature review of intelligent automation approaches using robotics, AI, and control for disassembly, inspection, sorting, and reprocessing of end-of-life electronics.