SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control
Safe and efficient shape-aware navigation in heterogeneous crowds and robot fleets remains challenging. Traditional approaches often assume homogeneous robots, sparse workspaces, simplified geometry, offline computation, or handcrafted parameters to make the problem tractable, which limits their deployment in dense crowd scenarios. Toward this end, we propose Shape-Aware Reinforcement Learned Model Predictive Control (SRL-MPC), a method for safe, efficient, and adaptive navigation in crowds with heterogeneous shapes without geometry simplification. To encode shape-aware safety, we formulate hi
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet-v1-1 →
“Fuzzy title match (0.94): “SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive ” ≈ “lllyasviel/ControlNet-v1-1””
- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet →
“Fuzzy title match (0.94): “SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive ” ≈ “lllyasviel/ControlNet””
- PossiblePossibly related (embedding) · 51%Gradient-based Planning for World Models at Longer Horizons →
- PossiblePossibly related (embedding) · 50%Physics-informed deep learning for robust trajectory prediction in automated driving - Nature →
- FuzzyOverlapping authors or contributors · 62%janhq/jan →
“Shared author/contributor keys: han”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%ultralytics/ultralytics →
“Shared author/contributor keys: han”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”