A reinforcement-learning policy that dynamically re-weights a spatial-temporal trajectory planner reduces collision counts during crowd navigation compared with fixed-weight planning and baseline learning methods.
Reciprocal velocity obsta- cles for real-time multi-agent navigation,
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Learning Dynamic Weight Adjustment for Spatial-Temporal Trajectory Planning in Crowd Navigation
A reinforcement-learning policy that dynamically re-weights a spatial-temporal trajectory planner reduces collision counts during crowd navigation compared with fixed-weight planning and baseline learning methods.