A DRL policy selects among base, arm, and whole-body kinematic models for NMPC on a mobile manipulator, improving success rate and reducing per-call computation time in simulation.
A survey of robotic motion planning in dynamic environments,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
A DRL policy selects among base, arm, and whole-body kinematic models for NMPC on a mobile manipulator, improving success rate and reducing per-call computation time in simulation.