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.
Tra- jectron++: Dynamically-feasible trajectory forecasting with heteroge- neous data,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
other 1
citation-polarity summary
fields
cs.RO 1years
2024 1verdicts
CONDITIONAL 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.